Shading Attenuation in Human Skin Color Images

Cavalcanti, Pablo G.; Scharcanski, Jacob; Lopes, Carlos Bruno Oliveira

doi:10.1007/978-3-642-17289-2_19

Cited by 39 publications

(33 citation statements)

References 6 publications

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“…Nowadays, many segmentation methods which could be split into different categories are widely used, for example: thresholding approaches, region-growing and region-merging approaches [24], classifiers, clustering approaches, Markov random field models, ANNs, deformable models [25,26], atlas-guided approaches [27], quadric functions with the implementation in order to avoid confusing between shadows and pigmented skin lesions [28], THz spectroscopic imaging and clustering algorithms [29], texture and fractal analysis [30,31], watershed technique [32], gradient vector flow algorithms [33] coherent analysis [34], melanoma recognition system method based on the ABCD criteria [35], support vector machines [36]. In addition to image segmentation tools as active contours and snakes, edge detection and clustering techniques based on thresholding were used [37].…”

Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

View full text Add to dashboard Cite

show abstract

“…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%

“…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.…”

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].…”

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confidence: 99%

See 2 more Smart Citations

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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Machine learning–based diagnosis of melanoma using macro images

Gautam

Ahmed

Meena

et al. 2018

Numer Methods Biomed Eng

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Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured through a dermatoscope. While designing a diagnostic model for general handheld imaging systems is an emerging trend, this article proposes a computer-aided decision support system for macro images captured by a general-purpose camera. General imaging conditions are adversely affected by nonuniform illumination, which further affects the extraction of relevant information. To mitigate it, we process an image to define a smooth illumination surface using the multistage illumination compensation approach, and the infected region is extracted using the proposed multimode segmentation method. The lesion information is numerated as a feature set comprising geometry, photometry, border series, and texture measures. The redundancy in feature set is reduced using information theory methods, and a classification boundary is modeled to distinguish benign and malignant samples using support vector machine, random forest, neural network, and fast discriminative mixed-membership-based naive Bayesian classifiers. Moreover, the experimental outcome is supported by hypothesis testing and boxplot representation for classification losses. The simulation results prove the significance of the proposed model that shows an improved performance as compared with competing arts.

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Shading Attenuation in Human Skin Color Images

Cited by 39 publications

References 6 publications

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

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

Machine learning–based diagnosis of melanoma using macro images

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