Automatic Segmentation of Skin Lesions Using Histogram Thresholding

Abstract

Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. Currently, there is a great interest in the development of Computer-Aided Diagnosis (CAD) systems for dermoscopic images. The segmentation step is one of the most important ones, since its accuracy determines the eventual success or failure of a CAD system. This study introduced new method of dermoscopic images segmentation. The preprocess was the filtering operation to dermoscopy image to remove most of difficulties facing the efficient segmentations, like a variety of lesion shapes, sizes, color, changes due to different skin types and textures and presence of hairs. Segmentation based mainly on histogram thresholding. The enhancements of image achieved by using mathematical morphology in order to obtain better segmentation with smooth border and without any noise in the lesion region. The proposed method evaluated by using Hammoude Distance (HM) and the True Detection Rate (TDR). Also the proposed method is compared with other skin lesions segmentation methods such as Otsu, adaptive thresholding and fuzzy Cmeans. The accuracy of proposed method was 96.32%, which is highly promised result and dependable.

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

“…Thresholding is used on multiple colors, a group of similar colors, or one-color channels derived by processing steps and primary component analysis [ 26 ]. Abbadi and Miry [ 27 ] proposed segmentation techniques, including the preprocessing stage, which assist in performing segments. Lu et al [ 28 ] joined thresholding approach of segmentation for boundary lesion image with the form of fuzzy C-means to last segmentation process.…”

Hybridization of CNN with LBP for Classification of Melanoma Images

“…Different methodologies and techniques are used to classify skin lesion images, [24] analyzed texture in skin images and found good results using baseline statistical methods. El Abbadi et al [31] Wiener filter is used to remove noise and thresholding is used to segment the whole images. Abd ElGhany et al [32] have used CNNs and different Fine-tuning methodologies for preprocessing and segmentation to achieve a plausible result.…”

Cilt Lezyon Bölütlemesi için Metasezgisel Temelli Otsu Eşikleme Yöntemi

“…[6], global eşikleme yerine görüntünün alt görüntülere bölündüğü ve alt görüntüler için ayrı ayrı eşik değerlerinin hesaplandığı adaptif bir otomatik eşikleme yöntemi önermiştir. [12], görüntünün histogramını çıkararak, histogram eşiklemesine dayalı bir otomatik eşikleme yöntemi önermiştir. [13], arıların yiyecek bulmada birbirleriyle haberleşmelerinden esinlenerek metasezgisel yöntem olan yapay arı kolonisi kullanan bir global eşikleme yöntemi önermiştir.…”