Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold

Fan, Haidi; Xie, Fengying; Yang, Li; Jiang, Zhiguo; Liu, Jie

doi:10.1016/j.compbiomed.2017.03.025

Cited by 116 publications

(86 citation statements)

References 28 publications

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“…It is important to note the difference between Figures 2(b) and 2(d) from the rectangular shapes. The segmentation algorithm computes object mean for those pixels within the rectangular patch that differ from background pixels following the assumption of color prior [13,40]. In Figure 2(b), not all pixels in the rectangular patch are object pixels, but in Figure 2(d) all pixels in the rectangular patch are object pixels, hence the principal reason for the observed difference in the rectangular shapes.…”

Section: Salient Pixel Computationmentioning

confidence: 99%

“…Complementing the methods of saliency computation with other useful analysis methods such as the morphological analysis can significantly improve image segmentation results. The hybrid segmentation of skin lesion in dermoscopic images using wavelet transform along with morphological analysis has been reported [1], while segmentation using saliency combined with Otsu threshold has been discussed [13].…”

Section: Saliency Based Segmentationmentioning

confidence: 99%

“…However, the application of saliency methods for segmentation of skin lesion is relatively new [13,17,37]. Saliency segmentation computes the most informative region in an image based on human vision perception such that salient and nonsalient parts become foreground region (skin lesion) and background region (healthy skin), respectively.…”

Section: Saliency Based Segmentationmentioning

confidence: 99%

“…Color prior assumes that background has uniform color while salient object colors are variegated. Brightness prior assumes that the brightness of background is higher than that of the salient object [13]. However, while methods based on these cognitive properties have performed well on certain images, they can fail to accurately detect salient objects that share uniform homogeneity with the background and for salient objects that touch the image border slightly [20].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, segmentation is one of the central stages for computer aided diagnosis of melanoma with dermoscopic images [13]. The automatic segmentation of skin lesion is particularly challenging because of the possible presence of undesirable factors in the form of skin hairs, specular 2 Mathematical Problems in Engineering reflections, variegated coloring, weak edges, low contrast, irregular and fuzzy borders, marker ink, color chart, ruler marks, dark corners, skin lines, blood vessels, and air or oil bubbles [10,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Salient Pixel Computationmentioning

confidence: 99%

Section: Saliency Based Segmentationmentioning

confidence: 99%

Section: Saliency Based Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

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2018

Mathematical Problems in Engineering

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Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification

Khan

Iskandar

Al‐Asad

et al. 2022

Concurrency and Computation

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Melanoma is a type of a skin cancer or lesion which has the detrimental ramifications on the human health but with early diagnosis it can be cured easily. The actual identification of skin lesion is very challenging because of factors like a very minute difference between lesion and skin and it is very difficult to differentiate among skin cancer types due to visual comparability. Hence an autonomous system for the diagnosis of true skin cancer type is very useful. In this article, we took the leverage of ensemble learning by combining the features of deep learning architectures with traditional features extraction approaches. For segmentation, we have two pipelines for the feature extraction. We extract the features through traditional split and merge approach as well as from deep learning algorithms of contextual encoding along with the attention mechanism. Later we combine the features of both architectures and predict the segmented region through intersection over union mechanism. After that segmented region is classified into three types of skin lesion using hybrid features of Alex-Net and VGG-16 through the transfer learning approach. The evaluation has been performed using the ISIC and PH2 datasets for which achieved segmentation accuracy is 97.8% and 96.7%, respectively. Moreover, hybrid classification network able to attain the 98.2% accuracy.

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Binary differential evolution with self learning and deep neural network for breast cancer classification

Pullaiah

Venkatasekhar

Venkatramana³

et al. 2022

Concurrency and Computation

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Early classification of breast cancer helps to treat the patient effectively and increases the survival rate. The existing methods involve applying the feature selection methods and deep learning methods to improve the performance of the breast cancer classification. In this research, the binary differential evolution with self learning (BDE-SL) and deep neural network (DNN) method is proposed to improve the performance of the breast cancer classification. The BDE-SL feature selection method involves selecting the relevant features based on the measure of probability difference for each feature and non-dominated sorting. The DNN method has the advantage which effectively analysis the non-linear relationship among the selected features and output.The BI-RADS MRI breast cancer dataset was applied to test the performance of the proposed method. The adaptive histogram equalization and region growing applied in the input images to enhance the image. The dual-tree complex wavelet transform, gray-level co-occurrence matrix, and local directional ternary pattern were the feature extraction method used for the classification. This result shows that BDE-SL with the DNN method has an accuracy of 99.12% and the existing convolutional neural network has 98.33% accuracy.

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Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold

Cited by 116 publications

References 28 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification

Binary differential evolution with self learning and deep neural network for breast cancer classification

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