2007
Other
Unknown
FINMECCANICA: Metodi di Elaborazione e Categorizzazione di Immagini e Applicazioni alla Diagnostica Industriale e Clinica
Colantonio S.
Not abstract available
Colantonio S.
Not abstract available
See at: CNR ExploRA
2007
Report
Unknown
Analysis and understanding of diagnostic images
Colantonio S.
This report describes the activities and summarized results obtained from the study and development of innovative methods for image recognition and understanding. A general methodology based on multi-level and multi-stage processing is discussed, analyzing the relevant aspects of each of the stages involved. Applications within diagnostic imaging are presented.Source: Project report, 2007
Colantonio S.
This report describes the activities and summarized results obtained from the study and development of innovative methods for image recognition and understanding. A general methodology based on multi-level and multi-stage processing is discussed, analyzing the relevant aspects of each of the stages involved. Applications within diagnostic imaging are presented.Source: Project report, 2007
See at: CNR ExploRA
2005
Report
Unknown
Categorization of digital images for applications in clinic and industrial diagnostics
Colantonio S.
This report summarized the activities carried out within the fellowship on "Image Processing and Analysis" and finalized to the definition, development and assessment of advanced techniques for the analysis and understanding of diagnostic images. Relevant aspects are addressed concerning Image Processing, Image Analysis and Representation, Image Recognition and Soft Computing Some applications within strategic fields such as biomedical diagnosis and quality control in industrial processes are also presented and discussed.Source: Project report, 2005
Colantonio S.
This report summarized the activities carried out within the fellowship on "Image Processing and Analysis" and finalized to the definition, development and assessment of advanced techniques for the analysis and understanding of diagnostic images. Relevant aspects are addressed concerning Image Processing, Image Analysis and Representation, Image Recognition and Soft Computing Some applications within strategic fields such as biomedical diagnosis and quality control in industrial processes are also presented and discussed.Source: Project report, 2005
See at: CNR ExploRA
2008
Doctoral thesis
Unknown
Mining image content and visual information. Theory and applications
Colantonio S.
Mining image content means to extract image hidden patterns, identify image data relationships and, thus, gather novel meaningful knowledge pertinent to the specific domain images belong to. Research in the field is still in its early stages, although it relies on rather assessed disciplines such as Computer Vision, Image Processing, Image Retrieval, Data Mining, Machine Learning, and Artificial Intelligence. The key importance that nowadays characterizes image-based tasks, i.e., tasks that relies on the management, analysis and interpretation of image content, is plainly perceivable in almost all the strategic social, scientific and industrial fields: an imaging investigation is a fundamental step of the medical diagnosis processes; in situ images are acquired for industrial inspection; biometric images are used in surveillance or forensic sciences; georeferenced imagery are gathered and employed in fields such as aerospace, defence, geophysics, intelligence, oceanography, and so forth. Advances in image acquisition and management technologies have fuelled the rapid growth of large and rich image collections, which can reveal meaningful information if suitably processed and exploited. Research in mining image content is just devoted to reach this goal. Image Mining can be seen as the summa and advancement of several processing procedures that are usually applied in image analysis. It requires a long chain that starts with image acquisition and storage, evolves through image processing, image content extraction and suitable representation, image retrieval and indexing, and ends up with the identification of meaningful patterns, thus allowing the production of novel knowledge relevant to the task to be solved. The fundamental challenge in Image Mining is to determine how low-level information contained in a raw image or image sequence can be processed to identify high-level information, and relationships among imagery data, as well as with other contextual data. This dissertation reports the investigation that was carried out in the field of Image Mining by facing several issues related to the different steps of the chain. Theoretical investigations were grounded into the development of innovative methods for tackling all the phases of the image mining process, from image content extraction, representation and browsing, to data mining for the generation of novel knowledge. Such methods were finally integrated into a framework able to support the main image mining functionalities, ranging from image storage to novel knowledge discovery. In accordance with the great value inherent in clinical images, and the increasing amount of digital images available in medical research, medical imaging was selected among the eligible application domains, and some case studies belonging to cardiology and microscopy were considered. More in detail, a novel two-step segmentation method was defined for extracting image structures. A methodological standardization of the features extraction process was proposed by developing a precise classification of the features and an ontological model of the domain. For image content interpretation, a knowledge-based system was developed for solving different types of image-based tasks, by integrating adaptive learning methods, image processing algorithms, inferential reasoning, and meta-level knowledge for strategic planning. Finally, all these methods were integrated into a general framework able to support the image mining chain.
Colantonio S.
Mining image content means to extract image hidden patterns, identify image data relationships and, thus, gather novel meaningful knowledge pertinent to the specific domain images belong to. Research in the field is still in its early stages, although it relies on rather assessed disciplines such as Computer Vision, Image Processing, Image Retrieval, Data Mining, Machine Learning, and Artificial Intelligence. The key importance that nowadays characterizes image-based tasks, i.e., tasks that relies on the management, analysis and interpretation of image content, is plainly perceivable in almost all the strategic social, scientific and industrial fields: an imaging investigation is a fundamental step of the medical diagnosis processes; in situ images are acquired for industrial inspection; biometric images are used in surveillance or forensic sciences; georeferenced imagery are gathered and employed in fields such as aerospace, defence, geophysics, intelligence, oceanography, and so forth. Advances in image acquisition and management technologies have fuelled the rapid growth of large and rich image collections, which can reveal meaningful information if suitably processed and exploited. Research in mining image content is just devoted to reach this goal. Image Mining can be seen as the summa and advancement of several processing procedures that are usually applied in image analysis. It requires a long chain that starts with image acquisition and storage, evolves through image processing, image content extraction and suitable representation, image retrieval and indexing, and ends up with the identification of meaningful patterns, thus allowing the production of novel knowledge relevant to the task to be solved. The fundamental challenge in Image Mining is to determine how low-level information contained in a raw image or image sequence can be processed to identify high-level information, and relationships among imagery data, as well as with other contextual data. This dissertation reports the investigation that was carried out in the field of Image Mining by facing several issues related to the different steps of the chain. Theoretical investigations were grounded into the development of innovative methods for tackling all the phases of the image mining process, from image content extraction, representation and browsing, to data mining for the generation of novel knowledge. Such methods were finally integrated into a framework able to support the main image mining functionalities, ranging from image storage to novel knowledge discovery. In accordance with the great value inherent in clinical images, and the increasing amount of digital images available in medical research, medical imaging was selected among the eligible application domains, and some case studies belonging to cardiology and microscopy were considered. More in detail, a novel two-step segmentation method was defined for extracting image structures. A methodological standardization of the features extraction process was proposed by developing a precise classification of the features and an ontological model of the domain. For image content interpretation, a knowledge-based system was developed for solving different types of image-based tasks, by integrating adaptive learning methods, image processing algorithms, inferential reasoning, and meta-level knowledge for strategic planning. Finally, all these methods were integrated into a general framework able to support the image mining chain.
See at: CNR ExploRA
2009
Bachelor thesis
Unknown
Mining image content and visual information. Theory and applications
Colantonio S.
Mining image content means to extract image hidden patterns, identify image data relationships and, thus, gather novel meaningful knowledge pertinent to the specific domain images belong to. Research in the field is still in its early stages, although it relies on rather assessed disciplines such as Computer Vision, Image Processing, Image Retrieval, Data Mining, Machine Learning, and Artificial Intelligence. The key importance that nowadays characterizes image-based tasks, i.e., tasks that relies on the management, analysis and interpretation of image content, is plainly perceivable in almost all the strategic social, scientific and industrial fields: an imaging investigation is a fundamental step of the medical diagnosis processes; in situ images are acquired for industrial inspection; biometric images are used in surveillance or forensic sciences; georeferenced imagery are gathered and employed in fields such as aerospace, defence, geophysics, intelligence, oceanography, and so forth. Advances in image acquisition and management technologies have fuelled the rapid growth of large and rich image collections, which can reveal meaningful information if suitably processed and exploited. Research in mining image content is just devoted to reach this goal. Image Mining can be seen as the summa and advancement of several processing procedures that are usually applied in image analysis. It requires a long chain that starts with image acquisition and storage, evolves through image processing, image content extraction and suitable representation, image retrieval and indexing, and ends up with the identification of meaningful patterns, thus allowing the production of novel knowledge relevant to the task to be solved. The fundamental challenge in Image Mining is to determine how low-level information contained in a raw image or image sequence can be processed to identify high-level information, and relationships among imagery data, as well as with other contextual data. This dissertation reports the investigation that was carried out in the field of Image Mining by facing several issues related to the different steps of the chain. Theoretical investigations were grounded into the development of innovative methods for tackling all the phases of the image mining process, from image content extraction, representation and browsing, to data mining for the generation of novel knowledge. Such methods were finally integrated into a framework able to support the main image mining functionalities, ranging from image storage to novel knowledge discovery. In accordance with the great value inherent in clinical images, and the increasing amount of digital images available in medical research, medical imaging was selected among the eligible application domains, and some case studies belonging to cardiology and microscopy were considered. More in detail, a novel two-step segmentation method was defined for extracting image structures. A methodological standardization of the features extraction process was proposed by developing a precise classification of the features and an ontological model of the domain. For image content interpretation, a knowledge-based system was developed for solving different types of image-based tasks, by integrating adaptive learning methods, image processing algorithms, inferential reasoning, and meta-level knowledge for strategic planning. Finally, all these methods were integrated into a general framework able to support the image mining chain.
Colantonio S.
Mining image content means to extract image hidden patterns, identify image data relationships and, thus, gather novel meaningful knowledge pertinent to the specific domain images belong to. Research in the field is still in its early stages, although it relies on rather assessed disciplines such as Computer Vision, Image Processing, Image Retrieval, Data Mining, Machine Learning, and Artificial Intelligence. The key importance that nowadays characterizes image-based tasks, i.e., tasks that relies on the management, analysis and interpretation of image content, is plainly perceivable in almost all the strategic social, scientific and industrial fields: an imaging investigation is a fundamental step of the medical diagnosis processes; in situ images are acquired for industrial inspection; biometric images are used in surveillance or forensic sciences; georeferenced imagery are gathered and employed in fields such as aerospace, defence, geophysics, intelligence, oceanography, and so forth. Advances in image acquisition and management technologies have fuelled the rapid growth of large and rich image collections, which can reveal meaningful information if suitably processed and exploited. Research in mining image content is just devoted to reach this goal. Image Mining can be seen as the summa and advancement of several processing procedures that are usually applied in image analysis. It requires a long chain that starts with image acquisition and storage, evolves through image processing, image content extraction and suitable representation, image retrieval and indexing, and ends up with the identification of meaningful patterns, thus allowing the production of novel knowledge relevant to the task to be solved. The fundamental challenge in Image Mining is to determine how low-level information contained in a raw image or image sequence can be processed to identify high-level information, and relationships among imagery data, as well as with other contextual data. This dissertation reports the investigation that was carried out in the field of Image Mining by facing several issues related to the different steps of the chain. Theoretical investigations were grounded into the development of innovative methods for tackling all the phases of the image mining process, from image content extraction, representation and browsing, to data mining for the generation of novel knowledge. Such methods were finally integrated into a framework able to support the main image mining functionalities, ranging from image storage to novel knowledge discovery. In accordance with the great value inherent in clinical images, and the increasing amount of digital images available in medical research, medical imaging was selected among the eligible application domains, and some case studies belonging to cardiology and microscopy were considered. More in detail, a novel two-step segmentation method was defined for extracting image structures. A methodological standardization of the features extraction process was proposed by developing a precise classification of the features and an ontological model of the domain. For image content interpretation, a knowledge-based system was developed for solving different types of image-based tasks, by integrating adaptive learning methods, image processing algorithms, inferential reasoning, and meta-level knowledge for strategic planning. Finally, all these methods were integrated into a general framework able to support the image mining chain.
See at: CNR ExploRA
2019
Contribution to journal
Open Access
See at:
ercim-news.ercim.eu 
| ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
Are active and assisted living applications addressing the main acceptance concerns of their beneficiaries? Preliminary insights from a scoping review
Colantonio S., Jovanovic M., Zdravevski E., Lameski P., Tellioglu H., Kampel M., Florez-Revuelta F.
Active and Assisted Living (AAL) technologies stand as a promising mean to respond to the big societal challenges related to health and social care. Nevertheless, despite their great potential and the recent boost ensured by the advances in Artificial Intelligence for data processing, the uptake in real-life settings of AAL technologies is still in its infancy. Several concerns seem to hinder the willingness of the targeted beneficiaries to integrate such technologies in their routines and living settings. Some studies and surveys have tried so far to identify and analyze these concerns and the factors that affect the immediate acceptance and long-term usage of AAL technologies, thus identifying accessibility, usability, privacy, safety, security and reliability as the core ones. Nevertheless, no attempts have been done yet to verify the reception of these analyses from a technological and implementation standpoint. This paper fills this gap by reporting the preliminary results of a scoping review of the AAL literature. The review investigates the solutions developed in the last five years that address various groups of beneficiaries and their concerns with respect to technology adoption. The results obtained aim to aid researchers, social and health care professionals, end users and technology providers understand the state of play of technological solutions, evaluation studies and the overall discussions that are appearing in the literature to address and respond to the end-users' concerns.Source: PETRA '22: 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 414–421, Corfù, Grecia, 29/06/2022 - 01/07/2022
DOI: 10.1145/3529190.3534753
Metrics:
Colantonio S., Jovanovic M., Zdravevski E., Lameski P., Tellioglu H., Kampel M., Florez-Revuelta F.
Active and Assisted Living (AAL) technologies stand as a promising mean to respond to the big societal challenges related to health and social care. Nevertheless, despite their great potential and the recent boost ensured by the advances in Artificial Intelligence for data processing, the uptake in real-life settings of AAL technologies is still in its infancy. Several concerns seem to hinder the willingness of the targeted beneficiaries to integrate such technologies in their routines and living settings. Some studies and surveys have tried so far to identify and analyze these concerns and the factors that affect the immediate acceptance and long-term usage of AAL technologies, thus identifying accessibility, usability, privacy, safety, security and reliability as the core ones. Nevertheless, no attempts have been done yet to verify the reception of these analyses from a technological and implementation standpoint. This paper fills this gap by reporting the preliminary results of a scoping review of the AAL literature. The review investigates the solutions developed in the last five years that address various groups of beneficiaries and their concerns with respect to technology adoption. The results obtained aim to aid researchers, social and health care professionals, end users and technology providers understand the state of play of technological solutions, evaluation studies and the overall discussions that are appearing in the literature to address and respond to the end-users' concerns.Source: PETRA '22: 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 414–421, Corfù, Grecia, 29/06/2022 - 01/07/2022
DOI: 10.1145/3529190.3534753
Metrics:
See at:
ISTI Repository | dl.acm.org 
| doi.org | CNR ExploRA
2022
Journal article
Open Access
Bedtime monitoring for fall detection and prevention in older adults
Ruiz J. F-B., Chaparro J. D., Romero M. J. S., Molina F. J. V., Garcia X. T., Peno C. B., Solano H. L., Colantonio S., Florez-Revuelta F., Lopez J. C.
Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.Source: International journal of environmental research and public health (Print) 19 (2022). doi:10.3390/ijerph19127139
DOI: 10.3390/ijerph19127139
Project(s): SHAPES
Metrics:
Ruiz J. F-B., Chaparro J. D., Romero M. J. S., Molina F. J. V., Garcia X. T., Peno C. B., Solano H. L., Colantonio S., Florez-Revuelta F., Lopez J. C.
Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.Source: International journal of environmental research and public health (Print) 19 (2022). doi:10.3390/ijerph19127139
DOI: 10.3390/ijerph19127139
Project(s): SHAPES
Metrics:
See at:
International Journal of Environmental Research and Public Health | ISTI Repository | www.mdpi.com | CNR ExploRA
2022
Journal article
Open Access
Ambient Assisted Living: a scoping review of artificial intelligence models, domains, technology and concerns
Jovanovic M., Mitrov G., Eftim Zdravevski, Lameski P., Colantonio S., Kampel M., Tellioglu H., Florez-Revuelta F.
Background: Ambient Assisted Living (AAL) is a common name for various Artificial Intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions towards the technology, it is critical to obtain clear and comprehensive insights concerning AI models employed, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study provides a scoping review of the literature on AI models in AAL. In particular, we analyze: 1) specific AI models employed in A?L systems, 2) the target domains of the models, 3) the technology using the models, and 4) the major concerns from the end-user perspective. Our goal is to consolidate research on the topic and inform end-users, healthcare professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: The study was conducted as a scoping review to identify, analyze and extract the relevant literature. It used a natural language processing (NLP) toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. The relevant articles were then extracted from the corpus and analyzed manually. The review included five digital libraries: the Institute of Electrical and Electronics Engineers (IEEE), PubMed, Springer, Elsevier, and the Multidisciplinary Digital Publishing Institute (MDPI). Results: The annual distribution of relevant articles shows a growing trend for all categories from January 2010 to November 2021. The AI models started with unsupervised approaches as the leader, followed by deep learning (dominant from 2020), instance-based learning, and supervised techniques. Activity recognition and assistance were the most common target domains of the models. Ambient sensing, wearable, and mobile technologies mainly implemented the models. Older adults were primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern, and to less extent, reliability, safety, privacy, and security. Conclusions: The study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should: involve healthcare professionals and caregivers as designers and users, comply with health-related regulation, improve transparency and privacy, integrate with healthcare technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines.Source: JMIR. Journal of medical internet research 24 (2022). doi:10.2196/36553
DOI: 10.2196/36553
Metrics:
Jovanovic M., Mitrov G., Eftim Zdravevski, Lameski P., Colantonio S., Kampel M., Tellioglu H., Florez-Revuelta F.
Background: Ambient Assisted Living (AAL) is a common name for various Artificial Intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions towards the technology, it is critical to obtain clear and comprehensive insights concerning AI models employed, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study provides a scoping review of the literature on AI models in AAL. In particular, we analyze: 1) specific AI models employed in A?L systems, 2) the target domains of the models, 3) the technology using the models, and 4) the major concerns from the end-user perspective. Our goal is to consolidate research on the topic and inform end-users, healthcare professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: The study was conducted as a scoping review to identify, analyze and extract the relevant literature. It used a natural language processing (NLP) toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. The relevant articles were then extracted from the corpus and analyzed manually. The review included five digital libraries: the Institute of Electrical and Electronics Engineers (IEEE), PubMed, Springer, Elsevier, and the Multidisciplinary Digital Publishing Institute (MDPI). Results: The annual distribution of relevant articles shows a growing trend for all categories from January 2010 to November 2021. The AI models started with unsupervised approaches as the leader, followed by deep learning (dominant from 2020), instance-based learning, and supervised techniques. Activity recognition and assistance were the most common target domains of the models. Ambient sensing, wearable, and mobile technologies mainly implemented the models. Older adults were primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern, and to less extent, reliability, safety, privacy, and security. Conclusions: The study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should: involve healthcare professionals and caregivers as designers and users, comply with health-related regulation, improve transparency and privacy, integrate with healthcare technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines.Source: JMIR. Journal of medical internet research 24 (2022). doi:10.2196/36553
DOI: 10.2196/36553
Metrics:
See at:
ISTI Repository | ISTI Repository | www.jmir.org | CNR ExploRA
2007
Contribution to book
Restricted
Automatic fuzzy-neural based segmentation of microscopic cell images
Colantonio S., Gurevich I. B., Salvetti O.
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for developing an automated application for the early diagnostics of lymphatic system tumours. The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the feasibility of the method.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Perner Petra, Salvetti Ovidio, pp. 115–127, 2007
DOI: 10.1007/978-3-540-76300-0
Metrics:
Colantonio S., Gurevich I. B., Salvetti O.
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for developing an automated application for the early diagnostics of lymphatic system tumours. The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the feasibility of the method.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Perner Petra, Salvetti Ovidio, pp. 115–127, 2007
DOI: 10.1007/978-3-540-76300-0
Metrics:
See at:
doi.org | www.springerlink.com | CNR ExploRA
2007
Journal article
Restricted
A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination
Colantonio S., Gurevich I. B., Salvetti O.
The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.Source: Pattern recognition and image analysis 17 (2007): 428–437. doi:10.1134/S1054661807030108
DOI: 10.1134/s1054661807030108
Metrics:
Colantonio S., Gurevich I. B., Salvetti O.
The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.Source: Pattern recognition and image analysis 17 (2007): 428–437. doi:10.1134/S1054661807030108
DOI: 10.1134/s1054661807030108
Metrics:
See at:
Pattern Recognition and Image Analysis | CNR ExploRA
2007
Journal article
Restricted
Microembolic signal characterization by transcranial Doppler imaging
Colantonio S., Salvetti O.
Transcranial Doppler detection and monitoring of cerebral microemboli have provided a new and useful method to diagnose, and potentially to foresee, increased risk of stroke. Until now, however, the assessment of this method in routine clinical practice has been limited by the lack of a reliable automatic differentiation between solid and gaseous microemboli. The aim of this work is the definition of a clinical diagnostic support procedure for the automatic recognition of emboli of different composition. The proposed method makes use of image processing techniques and neural algorithms for data interpretation and performs a feature-based analysis of the ultrasonographic images showing the microembolic events. Application to clinical cases selected by expert neurologists for their clinical relevance and experimental results have showed effective operability of the developed procedure.Source: Pattern recognition and image analysis 17 (2007): 567–577. doi:10.1134/S1054661807040165
DOI: 10.1134/s1054661807040165
Metrics:
Colantonio S., Salvetti O.
Transcranial Doppler detection and monitoring of cerebral microemboli have provided a new and useful method to diagnose, and potentially to foresee, increased risk of stroke. Until now, however, the assessment of this method in routine clinical practice has been limited by the lack of a reliable automatic differentiation between solid and gaseous microemboli. The aim of this work is the definition of a clinical diagnostic support procedure for the automatic recognition of emboli of different composition. The proposed method makes use of image processing techniques and neural algorithms for data interpretation and performs a feature-based analysis of the ultrasonographic images showing the microembolic events. Application to clinical cases selected by expert neurologists for their clinical relevance and experimental results have showed effective operability of the developed procedure.Source: Pattern recognition and image analysis 17 (2007): 567–577. doi:10.1134/S1054661807040165
DOI: 10.1134/s1054661807040165
Metrics:
See at:
Pattern Recognition and Image Analysis | www.springerlink.com | CNR ExploRA
2009
Journal article
Unknown
Diagnosis of lymphatic tumors by case-based reasoning on microscopic images
Colantonio S., Perner P., Salvetti O.
In this paper, a novel method for diagnosing lymphatic tissue tumors is presented. Microscopic specimen images are analyzed for extracting and characterizing malignant cells. A case-based reasoning approach is followed for classifying morphologic and densitometric cell features so as to provide a final diagnosis.Source: 2 (2009): 29–40.
Colantonio S., Perner P., Salvetti O.
In this paper, a novel method for diagnosing lymphatic tissue tumors is presented. Microscopic specimen images are analyzed for extracting and characterizing malignant cells. A case-based reasoning approach is followed for classifying morphologic and densitometric cell features so as to provide a final diagnosis.Source: 2 (2009): 29–40.
See at: CNR ExploRA
2009
Journal article
Restricted
An ontological framework for media analysis and mining
Colantonio S., Salvetti O., Gurevich I. B., Trusova Y.
Advances in tools and technologies for digital media production and analysis have assured the availability of larger and larger amount of data which carry a huge amount of information for solving specific application tasks. This development has stressed the need for advanced systems that are not limited to media storage and management but include also their intelligent representation and retrieval. In this paper, we report current results of an ontological framework under development for mining media data, thus offering the possibility of storing, retrieving, analyzing and investigating media to discover novel knowledge relevant to strategic application processes.Source: Pattern recognition and image analysis 19 (2009): 221–230. doi:10.1134/S1054661809020023
DOI: 10.1134/s1054661809020023
Metrics:
Colantonio S., Salvetti O., Gurevich I. B., Trusova Y.
Advances in tools and technologies for digital media production and analysis have assured the availability of larger and larger amount of data which carry a huge amount of information for solving specific application tasks. This development has stressed the need for advanced systems that are not limited to media storage and management but include also their intelligent representation and retrieval. In this paper, we report current results of an ontological framework under development for mining media data, thus offering the possibility of storing, retrieving, analyzing and investigating media to discover novel knowledge relevant to strategic application processes.Source: Pattern recognition and image analysis 19 (2009): 221–230. doi:10.1134/S1054661809020023
DOI: 10.1134/s1054661809020023
Metrics:
See at:
Pattern Recognition and Image Analysis | link.springer.com | CNR ExploRA
2010
Journal article
Open Access
Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications
Little S., Colantonio S., Salvetti O., Perner P.
Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Naïve Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.Source: Journal of software engineering and applications 3 (2010): 39–49. doi:10.4236/jsea.2010.31005
DOI: 10.4236/jsea.2010.31005
Metrics:
Little S., Colantonio S., Salvetti O., Perner P.
Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Naïve Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naïve Bayesian. Finally, we give an outlook for further work.Source: Journal of software engineering and applications 3 (2010): 39–49. doi:10.4236/jsea.2010.31005
DOI: 10.4236/jsea.2010.31005
Metrics:
See at:
Journal of Software Engineering and Applications | Journal of Software Engineering and Applications | www.scirp.org | CNR ExploRA
2004
Conference article
Unknown
Automatic recognition and classification of cerebral microemboli in ultrasound images
Colantonio S., Salvetti O., Sartucci F.
Aim of this work was the definition of a method devoted to the automated recognition of different composition of cerebral microemboli. The developed diagnostic procedure makes use of a features-based analysis of ultrasonographic images containing the characteristic microembolic signals. The images were acquired with a Transcranial Doppler, and classified using a Hierarchical Neural Network. The proposed procedure has been tested on clinical cases selected by expert neurologists for their relevance and experimental results have showed its reliability.Source: 7th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, pp. 647–650, St. Petersburg, 18-23 October 2004
Colantonio S., Salvetti O., Sartucci F.
Aim of this work was the definition of a method devoted to the automated recognition of different composition of cerebral microemboli. The developed diagnostic procedure makes use of a features-based analysis of ultrasonographic images containing the characteristic microembolic signals. The images were acquired with a Transcranial Doppler, and classified using a Hierarchical Neural Network. The proposed procedure has been tested on clinical cases selected by expert neurologists for their relevance and experimental results have showed its reliability.Source: 7th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, pp. 647–650, St. Petersburg, 18-23 October 2004
See at: CNR ExploRA
2006
Conference article
Unknown
Automatic fuzzy-neural based segmentation of microscopic cell images
Colantonio S., Gurevich I. B., Salvetti O.
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for develop-ing an automated application for the early diagnostics of lymphatic system tu-mors. The proposed method follows a two-step approach to, firstly, find the nu-clei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the fea-sibility of the method.Source: Workshop on Mass Data Analysis of Signals and Images, MDA 2006, pp. 34–45, Leipzig, Germany, 13/06/2006
Colantonio S., Gurevich I. B., Salvetti O.
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for develop-ing an automated application for the early diagnostics of lymphatic system tu-mors. The proposed method follows a two-step approach to, firstly, find the nu-clei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the fea-sibility of the method.Source: Workshop on Mass Data Analysis of Signals and Images, MDA 2006, pp. 34–45, Leipzig, Germany, 13/06/2006
See at: CNR ExploRA
2008
Conference article
Unknown
An Image Mining Medical Warehouse
Colantonio S., Gurevich I. B., Salvetti O., Trusova Y.
Advances in medical imaging technologies have assured the availability of more and more precise and detailed images whose analysis has became a necessary step in the diagnostic, prognostic and monitoring processes of main pathologies. Such development has stressed the need for advanced systems that are not limited to storage and management but include intelligent representation and retrieval of images. In this paper, we report current results of a medical warehouse we are developing for mining medical images, thus offering medical experts and researchers the possibility of storing, retrieving, analyzing and investigating biomedical images to discover novel knowledge relevant to diagnostic processes.Source: The First International Workshop on Image Mining: Theory and Applications (IMTA 200i8), pp. 83–92, Funchal, Madeira, Portugal, 22-23 January 2008
Colantonio S., Gurevich I. B., Salvetti O., Trusova Y.
Advances in medical imaging technologies have assured the availability of more and more precise and detailed images whose analysis has became a necessary step in the diagnostic, prognostic and monitoring processes of main pathologies. Such development has stressed the need for advanced systems that are not limited to storage and management but include intelligent representation and retrieval of images. In this paper, we report current results of a medical warehouse we are developing for mining medical images, thus offering medical experts and researchers the possibility of storing, retrieving, analyzing and investigating biomedical images to discover novel knowledge relevant to diagnostic processes.Source: The First International Workshop on Image Mining: Theory and Applications (IMTA 200i8), pp. 83–92, Funchal, Madeira, Portugal, 22-23 January 2008
See at: CNR ExploRA
2010
Contribution to book
Restricted
Prototype-based classification in unbalanced biomedical problems
Colantonio S., Little S., Salvetti O., Perner P.
Medical diagnosis can be easily assimilated to a classification problem devoted at identifying the presence or not of a disease. Since a pathology is often much rarer than the healthy condition, medical diagnosis may require a classifier to cope with the problem of under-represented classes. Class imbalance, which has revealed rather common in many other application domains, contravenes the traditional assumption of machine learning methods about the similar prior probabilities of target classes. In this respect, due to their unrestricted generalization ability, classifiers such as decision trees and Naïve Bayesian are not the proper classification methods. On the contrary, the basic feature of case-based classifiers to reason on representative samples of each class makes them appear a more suitable method for such a task. In this chapter, the behavior of a case-based classifier, ProtoClass, on unbalanced biomedical classification problems is evaluated in different settings of the case-base configuration. Comparison with other classification methods showed the effectiveness of such an approach to unbalanced classification problems and, hence, to medical diagnostic classification.Source: Successful Case-based Reasoning Applications, edited by Montani Stefania, Jain Lakhmi C., pp. 143–163. Berlin Heidelberg New York: Springer, 2010
DOI: 10.1007/978-3-642-14078-5_7
Metrics:
Colantonio S., Little S., Salvetti O., Perner P.
Medical diagnosis can be easily assimilated to a classification problem devoted at identifying the presence or not of a disease. Since a pathology is often much rarer than the healthy condition, medical diagnosis may require a classifier to cope with the problem of under-represented classes. Class imbalance, which has revealed rather common in many other application domains, contravenes the traditional assumption of machine learning methods about the similar prior probabilities of target classes. In this respect, due to their unrestricted generalization ability, classifiers such as decision trees and Naïve Bayesian are not the proper classification methods. On the contrary, the basic feature of case-based classifiers to reason on representative samples of each class makes them appear a more suitable method for such a task. In this chapter, the behavior of a case-based classifier, ProtoClass, on unbalanced biomedical classification problems is evaluated in different settings of the case-base configuration. Comparison with other classification methods showed the effectiveness of such an approach to unbalanced classification problems and, hence, to medical diagnostic classification.Source: Successful Case-based Reasoning Applications, edited by Montani Stefania, Jain Lakhmi C., pp. 143–163. Berlin Heidelberg New York: Springer, 2010
DOI: 10.1007/978-3-642-14078-5_7
Metrics:
See at:
doi.org | www.springerlink.com | CNR ExploRA
2007
Other
Unknown
Medical Image Mining: Theoretical Foundation and Technological Aspects - EU INTAS 04-77-7067
Colantonio S., Salvetti O.
Not abstract available
Colantonio S., Salvetti O.
Not abstract available
See at: CNR ExploRA