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In this paper, we present a classification method of dermoscopy images between melanocytic skin lesions (MSLs) and non-melanocytic skin lesions (NoMSLs). The motivation of this research is to develop a pre-processor of an automated melanoma screening system. Since NoMSLs have a wide variety of shapes and their border is often ambiguous, we developed a new tumor area extraction algorithm to account...
In this paper, we develop an automated method to recognize the dermoscopic criteria used for diagnosing melanomas as defined by two commonly used diagnostic schemes, namely the ABCD rule and the 7-point checklist. We use a database of 105 dermoscopy images and their dermoscopic findings determined by four dermatologists as the gold standard. We extract 356 objective image parameters from the images...
This research aims at finding the features that contribute to diagnose the illness as the malignant melanoma, based on the color information of images. We divide RGB space into 4096 pieces and choose the spaces including colors peculiar to malignant melanoma. By using these spaces, about 26% of images of melanoma could be taken out only by the color information. This is very useful to malignant melanoma...
We can observe Dots in dermoscopy images of pigmented skin lesions. Dots are dark small circular areas on pigmented skin lesion. Doctors take notice when Dots exist on border of tumor, count the Dots, and pay attention those scatter of size. This paper presents automatic detection for Dots. For the detection, we use closing operation, one of the morphological filters. But it has some faults to pick...
At present the diagnosis of melanoma is mainly performed based on the experience of each doctor. The doctors need some objective measure for diagnosis of melanoma and nevus. But there are few researches on objective index for the diagnosis. This workr deals with features of melanoma and nevus for computer diagnosis. First, we extracted the contour of lesions with image processing. One hundred five...
The purpose of this research is to classify the pattern on the surface of the nevus. The digital image that contains one nevus is classified into three kinds of patterns of homogeneous pattern, globular pattern, and reticular pattern by the texture analysis. The tumor part in the image is specified first, and the specified tumor part is divided into some sub-images. Afterwards, the amount of the texture...
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