| 1. |
- Gal, Yaniv, et al.
(författare)
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Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast
- 2009
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Ingår i: Proc. 2009 International Conference on Digital Image Computing: Techniques and Applications (DICTA). - 9781424452972 ; , s. 132 - 139
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Konferensbidrag (refereegranskat)abstract
- The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).
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| 2. |
- Parizi, Sobhan Naderi, et al.
(författare)
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Modeling Image Context using Object Centered Grid
- 2009
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Ingår i: 2009 DIGITAL IMAGE COMPUTING. - NEW YORK : IEEE. - 9781424452972 ; , s. 476-483
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Konferensbidrag (refereegranskat)abstract
- Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information as post-processing in order to index objects. Several recent computer vision approaches use contextual information to improve object recognition performance. They mainly use global information of the whole image by dividing the image into several pre-defined subregions, so called fixed grid. In this paper we propose an alternative approach to retrieval of contextual information, by customizing the location of the grid based on salient objects in the image. We claim this approach to result in more informative contextual features compared to the fixed-grid based strategy. To compare our results with the most relevant and recent papers, we use PASCAL 2007 data set. Our experimental results show an improvement in terms of Mean Average Precision.
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