Digital hair removal by deep learning for skin lesion segmentation

Li, Wei; Raj, Alex Noel Joseph; Tjahjadi, Tardi; Zhuang, Zhemin

doi:10.1016/j.patcog.2021.107994

Cited by 76 publications

(53 citation statements)

References 22 publications

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“…Most of the existing segmentation methods are highly dependent on various levels of preprocessing phases to circumvent the effects of undesired artifacts that could compromise the accurate segmentation of skin lesions. The occlusion resulting from undesirable artifacts can significantly hamper the accurate segmentation of skin lesions in dermoscopic images [ 34 ]. This challenge has led to the development of numerous artifact removal methods for occlusion in dermoscopic images.…”

Section: Related Studiesmentioning

confidence: 99%

“…The CLAHE is widely recognized as the best method among the prevailing enhancement methods for preprocessing of medical images [ 40 ]. In addition, literature has shown evidence of preprocessing stages based on histogram [ 33 ], mean subtraction [ 31 ], deep learning [ 34 ], multiscale decomposition [ 21 ], adaptive gamma correction [ 23 ], Z-score transformation [ 52 ], and Frangi Vesselness filter [ 41 ]. The artifact removal and image enhancement algorithms are generally executed before the actual segmentation and postprocessing methods are applied to suppress the leftover noise.…”

Section: Related Studiesmentioning

confidence: 99%

“…Myriads of image segmentation methods have been proposed in the literature for skin lesions. They include thresholding [ 17 , 19 ], clustering [ 20 , 21 , 22 , 23 ], statistical region merging [ 8 , 10 ], saliency [ 19 , 24 , 25 , 26 , 27 , 28 , 29 ], and deep learning [ 2 , 30 , 31 , 32 , 33 , 34 , 35 ]. Although multifarious image segmentation methods exist in the literature, accurate segmentation of skin lesions is still a challenging open problem because of the heterogeneous properties of dermoscopic images.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the existing studies have applied different image preprocessing methods to mitigate the debilitating effects of undesirable artifacts in dermoscopic images. These preprocessing methods include contrast enhancement [ 5 , 7 , 19 , 20 , 37 ], illumination correction [ 3 , 6 ], and hair removal [ 3 , 9 , 12 , 26 , 34 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…The preprocessing step is generally considered an effective way of achieving better segmentation results, but it compromises the processing speed. Besides, most of the existing segmentation methods are extremely dependent on the utilization of preprocessing methods, and related parameters to match the heterogeneous properties of input images can be hard to determine [ 30 , 34 ]. A generalized solitary method to handle the various types of undesirable artifacts is still an unresolved problem in the disciplines of image processing and computer vision.…”

Section: Introductionmentioning

confidence: 99%

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Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

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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.

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Section: Related Studiesmentioning

confidence: 99%