Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Abstract

Despite the recent advances in immune therapies, melanoma remains one of the deadliest and most difficult skin cancers to treat. Literature reports that multifarious driver oncogenes with tumor suppressor genes are responsible for melanoma progression and its complexity can be demonstrated by alterations in expression with signaling cascades. However, a further improvement in the therapeutic outcomes of the disease is highly anticipated with the aid of humanoid assistive technologies that are nowadays touted as a superlative alternative for the clinical diagnosis of diseases. The development of the projected technology-assistive diagnostics will be based on the innovations of medical imaging, artificial intelligence, and humanoid robots. Segmentation of skin lesions in dermoscopic images is an important requisite component of such a breakthrough innovation for an accurate melanoma diagnosis. However, most of the existing segmentation methods tend to perform poorly on dermoscopic images with undesirable heterogeneous properties. Novel image segmentation methods are aimed to address these undesirable heterogeneous properties of skin lesions with the help of image preprocessing methods. Nevertheless, these methods come with the extra cost of computational complexity and their performances are highly dependent on the preprocessing methods used to alleviate the deteriorating effects of the inherent artifacts. The overarching objective of this study is to investigate the effects of image preprocessing on the performance of a saliency segmentation method for skin lesions. The resulting method from the collaboration of color histogram clustering with Otsu thresholding is applied to demonstrate that preprocessing can be abolished in the saliency segmentation of skin lesions in dermoscopic images with heterogeneous properties. The color histogram clustering is used to automatically determine the initial clusters that represent homogenous regions in an input image. Subsequently, a saliency map is computed by agglutinating color contrast, contrast ratio, spatial feature, and central prior to efficiently detect regions of skin lesions in dermoscopic images. The final stage of the segmentation process is accomplished by applying Otsu thresholding followed by morphological analysis to obliterate the undesirable artifacts that may be present at the saliency detection stage. Extensive experiments were conducted on the available benchmarking datasets to validate the performance of the segmentation method. Experimental results generally indicate that it is passable to segment skin lesions in dermoscopic images without preprocessing because the applied segmentation method is ferociously competitive with each of the numerous leading supervised and unsupervised segmentation methods investigated in this study.

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

“…Diagnosis techniques depend on the computational analysis of dermoscopy images of skin lesions are the concentration of recent studies [5]. Previously this method had been considered to be challenging and of limited precision to the diverse range of skin tumours and the resulting images.…”

Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features

“…Several approaches for classifying skin lesions were examined and discovered that morphological closing is useful for enhancing classification model accuracy [24]. In applications involving medical imaging, the method has also been used with CLAHE [25]. A structuring element is used in morphological closing, which can be used to fill in gaps and remove small items from an image by first applying a dilation operation to it.…”

An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

“…The authors further utilised the two-dimensional entropy-based objective function and incorporated a two-dimensional histogram based on global means to illustrate the sample details. Another study highlighted the impacts of preprocessing on the saliency-based border estimation of medical images [ 33 ]. The authors merged colour histogram clustering (CHC) with the Otsu thresholding algorithm to achieve the objective.…”