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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...
SAM technology has been applied in medical and biological studies for 1) intra-operative pathological examinations since it does not require special staining techniques, 2) determining the origin of echo in clinical ultrasound imaging and 3) assessing the elastic properties of biological materials. In this short course, some examples of the application of scanning acoustic microscope (SAM) systems...
A half of melanomas in non-white populations are from acral volar area and an appearance of these lesions is completely different from that of other parts of lesions. However, no research for classifying these lesions has been conducted. In this paper, we describe a diagnosis classifier of acral volar melanomas. We used our dermatologist-like tumor area extraction algorithm and extracted a total of...
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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