Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

Abstract

Melanoma tumor can cause a serious life threatening problem in humans, if left untreated for a long time without early diagnosis. For early diagnosis of melanoma, it is more significant to develop novel methods based on biophysics analyses, molecular targets recognitions, and new image analysis criteria. In this article, anatomical region segmentation and diameter identification is proposed to detect melanoma from dermoscopic images. Four main steps of the proposed system are as follows: In the first step, the preprocessing is performed to smooth the melanoma extraction process. The region segmentation is done in the second step using watershed segmentation and Sobel operator. In the third step, the postprocessing procedures like as morphological open, canny edge detection also applied to improve the region of interest. Finally, the melanoma region is identified using color symmetry features. The proposed method is tested with two data sets to prove the performance proposed method. The proposed method achieved an accuracy of 95.31% and specificity of 98.3%, which is better than other methods. Experimental results show that the effectiveness of the proposed method and illustrate viability of real‐time clinical applications.

The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

“…This paper adopts an adaptive morphology-based extended minimum transformation technique (H-minima), which can extract the markers combined with prior knowledge. In essence, H-minima technology [ 30 , 31 , 32 ] removes local minima with a depth less than H by setting the parameter threshold value H, so selecting the threshold value is crucial to the extraction of markers. This paper uses H-minima technology to transform the gradient reconstruction image obtained in Section 3.1 .…”