The present study proposes a computer-aided diagnosis (CAD) system for the diagnosis of grades of fatty liver disease, namely mild, moderate and severe fatty liver along with normal liver tissue. Fifty-three B-mode ultrasound images consisting of 12 normal, 14 mild, 14 moderate and 13 severe fatty liver images are used. Based on the visual interpretations by the radiologists, region of interests (ROIs) from within the liver and one ROI from the diaphragm region are considered from each image. The texture features of these ROIs are combined in three ways to form ratio features, inverse ratio features and additive features. The sub-sets of optimal features are obtained by a differential evolution feature selection (DEFS) algorithm and a support vector machine (SVM) has been used for the classification task. The Laws ratio features have shown better performance with an average accuracy and standard deviation of 84.9±3.2. Hence, the CAD system could be useful to the radiologists in diagnosing grades of fatty liver disease.
The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.
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