Automatic Pill Identification from Pillbox Images

There is a vital need for fast and accurate recognition of medicinal tablets and capsules. Efforts to date have centered on automatic segmentation, color and shape identification. Our system combines these with preprocessing before imprint recognition. Using the National Library of Medicine Pillbox database, regression analysis applied to automatic color and shape recognition allows for successful pill identification. Measured errors for the subtasks of segmentation and color recognition for this database are 1.9% and 2.2%, respectively. Imprint recognition with optical character recognition (OCR) is key to exact pill ID, but remains a challenging problem, therefore overall recognition accuracy is not yet known

Detection of Basal Cell Carcinoma using Color and Histogram Measures of Semitranslucent Areas

Background: Semitranslucency, defined as a smooth, jelly-like area with varied, near-skin-tone color, can indicate a diagnosis of basal cell carcinoma (BCC) with high specificity. This study sought to analyze potential areas of semitranslucency with histogram-derived texture and color measures to discriminate BCC from non-semitranslucent areas in non-BCC skin lesions. Methods: For 210 dermoscopy images, the areas of semitranslucency in 42 BCCs and comparable areas of smoothness and color in 168 non-BCCs were selected manually. Six color measures and six texture measures were applied to the semitranslucent areas of the BCC and the comparable areas in the non-BCC images. Results: Receiver operating characteristic (ROC)curve analysis showed that the texture measures alone provided greater separation of BCC from non-BCC than the color measures alone. Statistical analysis showed that the four most important measures of semitranslucency are three histogram measures: contrast, smoothness, and entropy, and one color measure: blue chromaticity. Smoothness is the single most important measure. the combined 12 measures achieved a diagnostic accuracy of 95.05% based on area under the ROC curve. Conclusion: Texture and color analysis measures, especially smoothness, may afford automatic detection of BCC images with semitranslucency

Detection of Granularity in Dermoscopy Images of Malignant Melanoma using Color and Texture Features

Granularity, also called peppering and multiple blue-grey dots, is defined as an accumulation of tiny, blue-grey granules in dermoscopy images. Granularity is most closely associated with a diagnosis of malignant melanoma. This study analyzes areas of granularity with color and texture measures to discriminate granularity in melanoma from similar areas in non-melanoma skin lesions. The granular areas in dermoscopy images of 74 melanomas and 14 melanomas in situ were identified and manually selected. For 200 non-melanoma dermoscopy images, those areas which most closely resembled granularity in color and texture were similarly selected. Ten texture and twenty-two color measures were studied. The texture measures consisted of the average and range of energy, inertia, correlation, inverse difference, and entropy. The color measures consisted of absolute and relative RGB averages, absolute and relative RGB chromaticity averages, absolute and relative G/B averages, CIE X, Y, Z, X/Y, X/Z and Y/Z averages, R variance, and luminance. These measures were calculated for each granular area of the melanomas and the comparable areas in the non-melanoma images. Receiver operating characteristic (ROC) curve analysis showed that the best separation of melanoma images from non-melanoma images by granular area features was obtained with a combination of color and texture measures. Comparison of ROC results showed greater separation of melanoma from benign lesions using relative color than using absolute color. Statistical analysis showed that the four most significant measures of granularity in melanoma are two color measures and two texture measures averaged over the spots: relative blue, relative green, texture correlation, and texture energy range. The best feature set, utilizing texture and relative color measures, achieved an accuracy of 96.4% based on area under the receiver operating characteristic curve