Segmentation of Dermoscopy Images Using Wavelet Networks

Sadri, Amir; Zekri, Maryam; Sadri, Saeed; Gheissari, Niloofar; Mokhtari, Mojgan; Kolahdouzan, Farzaneh

doi:10.1109/tbme.2012.2227478

Cited by 74 publications

(40 citation statements)

References 42 publications

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“…This study applies the precision ( ), recall ( ), accuracy ( ), and dice ( ) evaluation metrics to quantitatively score the binary segmentation results computed by the comparative algorithms. These evaluation metrics are widely used for judging the performance of binary segmentation algorithms [8,13,19,20,47,48,61,[83][84][85]. A binary segmentation algorithm with satisfactory performance has high precision, recall, accuracy, and dice values.…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

“…True negative (T n ) is the count of healthy skin pixels correctly identified as healthy skin. These measures are mathematically defined as follows [13,47]:…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

“…The segmentation of skin lesion in dermoscopic images using wavelet networks considers the , , and channels of the color model as the network inputs and network structure formation [47]. The segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological analysis found the channel of the color model to give better performance than grayscale conversion [1].…”

Section: Color Image Modelsmentioning

confidence: 99%

See 2 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: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

“…True negative (T n ) is the count of healthy skin pixels correctly identified as healthy skin. These measures are mathematically defined as follows [13,47]:…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

Section: Color Image Modelsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…A wide range of algorithms has been used for image segmentation, broadly categorized as pixel-based segmentation, region-based segmentation and edge detection (Joel et al, 2002;Mendonca et al, 2007;Sadri et al, 2013;Toossi et al, 2013;Wang et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

et al. 2016

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Introduction: Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods: This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results: With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions: The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.

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“…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]. Here we are only considering methods that use non-dermoscopic images.…”

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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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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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Segmentation of Dermoscopy Images Using Wavelet Networks

Cited by 74 publications

References 42 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

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

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

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