Automated prescreening of pigmented skin lesions using standard cameras

Cavalcanti, Pablo G.; Scharcanski, Jacob

doi:10.1016/j.compmedimag.2011.02.007

Cited by 120 publications

(73 citation statements)

References 20 publications

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“…Also our method has the second highest specificity tested on melanoma cases. Otsu-PCA [17] has a better specificity in the melanoma group with a narrow lead of 0.7 percent; however our result in sensitivity leads over that method's score by 13.3 percent. Middle column: results of TDLS method [18].…”

Section: Quantitative Evaluationcontrasting

confidence: 47%

“…Finally, our method is compared with five other methods that exist in the literature for skin lesion segmentation. The compared methods are namely as L-SRM [13], Otsu-R [15], Otsu-RGB [16], Otsu-PCA [17] and TDLS [18]. The numerical evaluation of these methods based on mentioned metrics on the same dataset is reported from [18].…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…Because of the mentioned difficulties, such as illumination variation, presence of artifacts, and low contrast between lesion and normal skin, some methods are specifically proposed for lesion segmentation in these images [15][16][17][18]. Some of the methods consider color information [15][16][17]. Work of [15] considers the difference between the lesion and normal skin parts in red channel of RGB color space and performs thresholding to segment the image.…”

mentioning

confidence: 99%

“…Most of the segmentation algorithms are vulnerable to this misleading factor and they make incorrect segmentation. The mentioned methods [15][16][17][18] comprise a preprocessing step that deals with illumination variations. A review of various methods, for illumination modelling and correction, is given in [19].…”

mentioning

confidence: 99%

“…Work of [15] considers the difference between the lesion and normal skin parts in red channel of RGB color space and performs thresholding to segment the image. Method of [16] performs on multiple color channels and [17] utilizes a discriminating 3-channel space that is calculated by principle component analysis (PCA). However, color based methods fail to notice the textural information such as smoothness or raggedness that could aid the segmentation procedure.…”

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confidence: 99%

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Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

et al. 2017

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Abstract-Melanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion's region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion's border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.Index Terms-Convolutional neural network, deep learning, medical image segmentation, melanoma, skin cancer.

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Section: Quantitative Evaluationcontrasting

confidence: 47%

Section: Quantitative Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

et al. 2017

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Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

Chakkaravarthy

Chandrasekar²

2020

Int J Imaging Syst Tech

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Melanoma tumor can cause a serious life threatening problem in humans, if left untreated for a long time without early diagnosis. For early diagnosis of melanoma, it is more significant to develop novel methods based on biophysics analyses, molecular targets recognitions, and new image analysis criteria. In this article, anatomical region segmentation and diameter identification is proposed to detect melanoma from dermoscopic images. Four main steps of the proposed system are as follows: In the first step, the preprocessing is performed to smooth the melanoma extraction process. The region segmentation is done in the second step using watershed segmentation and Sobel operator. In the third step, the postprocessing procedures like as morphological open, canny edge detection also applied to improve the region of interest. Finally, the melanoma region is identified using color symmetry features. The proposed method is tested with two data sets to prove the performance proposed method. The proposed method achieved an accuracy of 95.31% and specificity of 98.3%, which is better than other methods. Experimental results show that the effectiveness of the proposed method and illustrate viability of real‐time clinical applications.

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Skin lesion segmentation based on mask RCNN, Multi Atrous Full‐CNN, and a geodesic method

Bagheri

Tarokh

Ziaratban

2021

Int J Imaging Syst Tech

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Automatic accurate skin lesion segmentation systems are very helpful for timely diagnosis and treatment of skin cancers. Recently, methods based on convolutional neural networks (CNN) have presented powerful performances and good results in biomedical applications. In the proposed method, a novel structure based on Mask RCNN, a proposed CNN, and a geodesic segmentation method is presented to improve the performance of the skin lesion segmentation. Lesions are detected and segmented by the Mask R-CNN in the first stage. A multi-atrous full convolutional neural network (MAFCNN) is proposed to combine outputs of the Mask RCNN and the input image to present more accurate segmentation results. To modify boundary of the lesion segmented by the MAFCNN, a geodesic segmentation method is used. Some parts of the segmentation result of the proposed CNN are utilized as labeled pixels for the geodesic method. Results demonstrate that using the proposed MAFCNN in a novel structure followed by the geodesic method significantly improves the mean Jaccard value. Experiments on five well-known skin image datasets show that the proposed method outperforms other state-of-the-art methods.

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Automated prescreening of pigmented skin lesions using standard cameras

Cited by 120 publications

References 20 publications

Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

Skin lesion segmentation based on mask RCNN, Multi Atrous Full‐CNN, and a geodesic method

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