Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Abstract

The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

“…CV is a bounding box based semiautomated technique implemented by Chan and Vese to extract the information from the test pictures [9]. Recently, CV is widely adopted to extract the chosen regions of medicinal image under assessment [43][44][45]. The working of CV is similar to the level-set procedure, in which the edges of the box are corrected incessantly based on the allocated iteration quantity.…”

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

“…The proposed technique was evaluated in four different public datasets images, PH2 [4], ISBI 2016 challenge [5], ISBI 2017 challenge [30], and DermIS datasets [31]. An outline of the publicly available dermatological datasets is summarized in Table 1.…”

“…The database consists of a medical explanatory note for every image depending on the medical segmentation of the tested region and including the ground truth images. The manual partition of the lesions area and dermoscopic norm (ground truth) were implemented using a proficient dermatologist [4].…”

“…Figure 1 shows different dermoscopy images with various deviations and artifacts. On the other hand, segmentation is challenging because of the great variety of lesion forms, dimensions, the presence of dark hair and colors along with various skin types and textures [4,5].…”

“…Finally, the estimated optimum threshold value was used for the melanoma segmentation like the thresholding method as used in the Otsu method [29]. To show the robustness of this work, the proposed approach was evaluated for four public dermoscopy image datasets: PH2 [4], ISBI2016 challenge [5], ISBI 2017 challenge [30], and Dermis datasets [31].…”