Border detection in dermoscopy images using statistical region merging

Celebi, M. Emre; Kingravi, Hassan A.; Iyatomi, Hitoshi; Aslandogan, Y. Alp; Stoecker, William V.; Moss, Randy Hays; Malters, Joseph M.; Grichnik, James M.; Marghoob, Ashfaq A.; Rabinovitz, Harold; Menzies, Scott W.

doi:10.1111/j.1600-0846.2008.00301.x

Cited by 304 publications

(202 citation statements)

References 18 publications

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“…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%

“…Dermatologically important aspects such as lesion asymmetry and border irregularity are mainly derived from the segmentation mask. Various segmentation methods exist for this purpose, which can be classified into three groups [12] as active contours, region merging [13] and thresholding. A review of existing methods for segmentation of dermoscopic images is given in [12] and [14].…”

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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 Evaluationmentioning

confidence: 99%

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confidence: 99%

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

et al. 2017

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“…Celebi et al [9] presented a method to segment skin lesion in dermoscopy images through statistical region merging method. The method is a technique developed to segment images based on region growing and merging.…”

Section: Previous Workmentioning

confidence: 99%

“…Many methods have been applied for hair removal and they were mostly based on adaptive thresholding and morphological operations or used median filter method [9]. The directional Gabor filters are implemented to extract the hairs from dermoscopy images.…”

Section: Hair Detection and Inpaintingmentioning

confidence: 99%

Detection of Melanoma Skin Cancer in Dermoscopy Images

Eltayef

Liu

2017

J. Phys.: Conf. Ser.

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Abstract. Malignant melanoma is the most hazardous type of human skin cancer and its incidence has been rapidly increasing. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. In this paper, we present a novel method for the detection of melanoma skin cancer. To detect the hair and several noises from images, pre-processing step is carried out by applying a bank of directional filters. And therefore, Image inpainting method is implemented to fill in the unknown regions. Fuzzy C-Means and Markov Random Field methods are used to delineate the border of the lesion area in the images. The method was evaluated on a dataset of 200 dermoscopic images, and superior results were produced compared to alternative methods.

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“…This is an active research topic of medical imaging, and many methods have been developed over time [6]. Segmentation of skin lesions may be performed using a number of techniques, which take advantage of the skin homogeneity in the domain of color, luminance or texture, and they include statistical region merging [26,27], dynamic programming [28], and wavelet-based texture analysis [7]. The segmentation phase is followed by shape analysis to investigate the lesion type [29].…”

Section: Related Literaturementioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

Kawulok

Nalepa

et al. 2014

EURASIP J. Adv. Signal Process.

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In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

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Border detection in dermoscopy images using statistical region merging

Cited by 304 publications

References 18 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

Detection of Melanoma Skin Cancer in Dermoscopy Images

Self-adaptive algorithm for segmenting skin regions

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