Hair detection and lesion segmentation in dermoscopic images using domain knowledge

Pathan, Sameena; Prabhu, Kartik; Siddalingaswamy, P. C.

doi:10.1007/s11517-018-1837-9

Cited by 29 publications

(16 citation statements)

References 35 publications

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“…Whereas, for image size 512 x 512, the average accuracy is 93.79% with average error rate 6.21%. The segmentation results of PH2 are also compared with existing methods such as (Pennisi et al, ) and (Pathan, Prabhu, & Siddalingaswamy, ) which shows that proposed method perform superior.…”

Section: Experimental Results and Analysismentioning

confidence: 92%

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Afza

Khan

Sharif

et al. 2019

Microscopy Res & Technique

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Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co‐occurrences matrices features are extracted and covariance‐based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.

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Section: Experimental Results and Analysismentioning

confidence: 92%

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Afza

Khan

Sharif

et al. 2019

Microscopy Res & Technique

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“…This is in disparity with the findings of studies, which stated that preprocessing is needed for efficient and robust analysis of skin lesions in dermoscopic images [ 17 , 21 , 34 ]. A plethora of studies have showed the huge impact of preprocessing to significantly increase the skin lesion segmentation results [ 9 , 21 , 23 , 34 , 40 , 47 , 69 , 87 , 89 , 90 ]. Conversely, the results of this study support the findings of [ 91 ] that the preprocessing effect is dependent on segmentation and postprocessing methods employed.…”

Section: Discussionmentioning

confidence: 99%

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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“…However, among all, detection of lesions from dermoscopy images is a challenging task from the last few years, and various techniques are developed by several researchers using computer vision (CV) and machine learning (Amin, Sharif, Yasmin, Saba, & Raza, ; Amin et al, ; Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Iqbal et al, ; Iqbal, Ghani, Saba, & Rehman, ; Iqbal, Khan, Saba, & Rehman, ; Jamal et al, ; Fahad et al, ). Pathan, Prabhu, and Siddalingaswamy () developed a novel hair detection and segmentation method by utilizing specific properties of the lesion. Their work is composed of two parts.…”

Section: Related Workmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

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Hair detection and lesion segmentation in dermoscopic images using domain knowledge

Cited by 29 publications

References 35 publications

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

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