Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

Abstract

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. In this paper, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thresholding methods. Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state-of-the-art methods.

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

“…Thresholding approach is the most common procedure used in different applications, for example, in biomedical image analysis [41,42], handwritten character identification [43], automatic target recognition [44], change-detection applications [45–47], reconstruction of a map of interference fringes [48], and segmentation based on colour images [49]. Colour is one of the most significant low-level feature that can be used to extract homogeneous regions which are related to objects or part of objects most of the time, multilevel thresholding technique approaches [50,51], thresholding approach in Otsu algorithm [52], threshold approach in segmentation of satellite images [53], and other applications [15,54].…”

Automatic multiple regions segmentation of dermoscopy images

“…Furthermore, extraction of other important clinical patterns such as pigment network, dots/globules, streaks, regression areas, and bluewhitish veil critically depends on the accuracy of border detection [3]. Automated border detection of skin lesions is a challenging step in early skin cancer detection because of several reasons such as: (1) low contrast between the lesion and the surrounding skin with varying levels of saturation and intensity, (2) ring-shaped lesions, (3) irregular and unclear lesion boundaries, (4) asymmetry in color and shape, and (5) artifacts such as black frames, hairs, and air bubbles.…”

Is the Fractal Dimension of the Contour-lines a Reliable Tool for Classification of Medical Images?

“…After establishing and configuring the software tools, we considered a set of 100 biomedical images of melanoma and non-melanoma moles, most of them obtained with the kind allowance of the first author of [18]- [21]. The images (out of which 30 are invasive malignant melanoma and 70 benign) are obtained from the EDRA Interactive Atlas of Dermoscopy and the dermatology practices of Dr. Ashfaq Marghoob (New York, NY), Dr. Harold Rabinovitz (Plantation, FL), and Dr. Scott Menzies (Sydney, Australia).…”