Alina Dumitrescu - Academia.edu (original) (raw)

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Papers by Alina Dumitrescu

Research paper thumbnail of Attachment Disturbances in Young Children. II: Indiscriminate Behavior and Institutional Care

Journal of The American Academy of Child and Adolescent Psychiatry, 2002

Research paper thumbnail of Attachment Disturbances in Young Children. I: The Continuum of Caretaking Casualty

Journal of The American Academy of Child and Adolescent Psychiatry, 2002

Research paper thumbnail of Juvenile xanthogranulomatosis with bilateral and multifocal ocular lesions of the iris, cornealscleral limbus, and choroid

Research paper thumbnail of Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans

Medical Image Analysis

Contextual information plays an important role in medical image understanding. Medical experts ma... more Contextual information plays an important role in medical image understanding. Medical experts make use of context to detect and differentiate pathologies in medical images, especially when interpreting difficult cases. The majority of computer-aided diagnosis (CAD) systems, however, employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this paper, we present a generic system for including contextual information in a CAD system. Context is described by means of high-level features based on the spatial relation between lesion candidates and surrounding anatomical landmarks and lesions of different classes (static contextual features) and lesions of the same type (dynamic contextual features). We demonstrate the added value of contextual CAD for two real-world CAD tasks: the identification of exudates and drusen in 2D retinal images and coronary calcifications in 3D computed tomography scans. Results show that in both applications contextual CAD is superior to a local CAD approach with a significant increase of the figure of merit of the Free Receiver Operating Characteristic curve from 0.84 to 0.92 and from 0.88 to 0.98 for exudates and drusen, respectively, and from 0.87 to 0.93 for coronary calcifications.FROC curves for the classification performance of hard exudates using local and contextual CAD systems. The diamond represents the result of the second human observer. The performance of a majority CAD system is also included. This system classifies the candidate in the same class as the majority of the candidates in the neighborhood present after local classification. Additionally, the performance of a contextual CAD system using only the proximity to red lesions as contextual information is included. The horizontal axis has a logarithmic scale.► Exploiting contextual information for lesion detection in medical images improve significantly the final classification. ► Contextual information helps to solve ambiguities in lesion classification. ► Exploiting contextual information makes the method more robust against segmentation errors. ► Using contextual information, the CAD system reached the performance obtained by human observers. ► Combining local and contextual information helps to emulate human cognitive process in medical image understanding.

Research paper thumbnail of Attachment Disturbances in Young Children. II: Indiscriminate Behavior and Institutional Care

Journal of The American Academy of Child and Adolescent Psychiatry, 2002

Research paper thumbnail of Attachment Disturbances in Young Children. I: The Continuum of Caretaking Casualty

Journal of The American Academy of Child and Adolescent Psychiatry, 2002

Research paper thumbnail of Juvenile xanthogranulomatosis with bilateral and multifocal ocular lesions of the iris, cornealscleral limbus, and choroid

Research paper thumbnail of Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans

Medical Image Analysis

Contextual information plays an important role in medical image understanding. Medical experts ma... more Contextual information plays an important role in medical image understanding. Medical experts make use of context to detect and differentiate pathologies in medical images, especially when interpreting difficult cases. The majority of computer-aided diagnosis (CAD) systems, however, employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this paper, we present a generic system for including contextual information in a CAD system. Context is described by means of high-level features based on the spatial relation between lesion candidates and surrounding anatomical landmarks and lesions of different classes (static contextual features) and lesions of the same type (dynamic contextual features). We demonstrate the added value of contextual CAD for two real-world CAD tasks: the identification of exudates and drusen in 2D retinal images and coronary calcifications in 3D computed tomography scans. Results show that in both applications contextual CAD is superior to a local CAD approach with a significant increase of the figure of merit of the Free Receiver Operating Characteristic curve from 0.84 to 0.92 and from 0.88 to 0.98 for exudates and drusen, respectively, and from 0.87 to 0.93 for coronary calcifications.FROC curves for the classification performance of hard exudates using local and contextual CAD systems. The diamond represents the result of the second human observer. The performance of a majority CAD system is also included. This system classifies the candidate in the same class as the majority of the candidates in the neighborhood present after local classification. Additionally, the performance of a contextual CAD system using only the proximity to red lesions as contextual information is included. The horizontal axis has a logarithmic scale.► Exploiting contextual information for lesion detection in medical images improve significantly the final classification. ► Contextual information helps to solve ambiguities in lesion classification. ► Exploiting contextual information makes the method more robust against segmentation errors. ► Using contextual information, the CAD system reached the performance obtained by human observers. ► Combining local and contextual information helps to emulate human cognitive process in medical image understanding.

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