Abstract
Dermoscopic images suffer from irregular and vague boundaries. New directions established the neutrosophic set (NS) approaches for clustering, and segmenting the dermoscopic images. In this work, an accurate segmentation process was developed by mapping initially the dermoscopic images to the NS domain. Thus, the neutrosophic image was defined by three subsets, namely True (T), Indeterminacy (I) and False (F). For accurate boundary detection and segmentation, different high pass (HP) filter types were used in the definition of I subset and low pass (LP) filter types in the definition of T. These filters form a new way to obtain an NS image for segmenting dermoscopy images. A comparative study was carried on the ISIC2016 skin lesion dermoscopic images dataset using different combinations of NS filter types and sizes. The results depicted the superiority of using an unsharp filter in implementing the I subset and an average filter for the T subset. 96% segmentation accuracy was reported using the proposed design compared to 92% accuracy using the default NS definition.
Highlights
One of the most challenging tasks in healthcare is the accurate diagnosis due to the dependency on the physicians’ experience along with the fuzziness in the medical images
The high pass (HP) filters are used for image enhancement, while the low pass (LP) filters are used for smoothing and noise suppression
The unsharp filter has better performance to enhance and sharpen the high frequency components in the images compared to other HP filters, such as Prewitt, Sobel, and kernel operators [22]
Summary
One of the most challenging tasks in healthcare is the accurate diagnosis due to the dependency on the physicians’ experience along with the fuzziness in the medical images. Several medical image processing procedures, such as denoising, clustering, segmentation, and classification, have been presented based on fuzzy theory to infer the intrinsic vagueness, ambiguity, and uncertainty [1]–[3]. Fuzzy-based approaches are sensitive to the artifacts and noise; do not deliberate the pixels’ spatial context [4]. To overcome this drawback, the neutrosophic concept which introduced by Smarandache as a generalization of the fuzzy set [5], [6], was applied.
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