Saliency-based segmentation of dermoscopic images using color information (original) (raw)
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Saliency-based segmentation of dermoscopic images using colour information
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how to use color information, besides saliency, for determining the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the input image data distribution. As a means of refining the accuracy of the proposed method, the segmentation step is preceded by a pre-processing aimed at reducing the computation burden, removing artifacts, and improving contrast. We have assessed the method on two public databases, including 1497 dermoscopic images. We have also compared its performance with classical and recent saliency-based methods designed explicitly for dermoscopic images. The qualitative and quantitative evaluation indicates that the proposed method is promising since it produces an accurate skin lesion segmentation and performs satisfactorily compared to other existing saliency-based segmentation methods.
Automatic Skin Lesion Segmentation based on Saliency and Color
Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020
Segmenting skin lesions in dermoscopic images is a key step for the automatic diagnosis of melanoma. In this framework, this paper presents a new algorithm that after a pre-processing phase aimed at reducing the computation burden, removing artifacts and improving contrast, selects the skin lesion pixels in terms of their saliency and color. The method is tested on a publicly available dataset and is evaluated both qualitatively and quantitatively.
Mathematical Problems in Engineering
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.
A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification
Computers, Materials & Continua
Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject's survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two methods simultaneously; one is the weighted visual saliency-based method, and the second is improved HDCT based saliency estimation. The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region. The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer learning. The simulation results show improved performance compared to several existing methods.
Color approach of melanoma lesion segmentation
This paper presents a segmentation approach of melanoma by color morphology. The images of melanoma are filtered by morphological tools using a lexicographic order onto HSI color space. A thresholding technique is applied to segment the melanoma Region of Interest (ROI). Binary morphological techniques are used to filter the ROI. The approach was tested on two benign and malignant image databases, both containing 100 images, and the results were compared to ground-truth segmentation and Fuzzy CMeans one. By performing twelve metrics, the results have shown the promising aspects of this approach to segment benign and malignant lesions.
Journal of Ambient Intelligence and Humanized Computing, 2018
The number of deaths caused by melanoma has increased remarkably in the last few years which are the carcinogenic type of skin cancer. Lately, computer based methods are introduced which are intelligent enough to support dermatologist in initial judgment of skin lesion. However, there still exists a gap for an optimal solution; therefore, machine learning community is still considering it a great challenge. The primary objective of this article is to efficiently detect and classify skin lesion with the utilization of an improved segmentation and feature selection criteria. Presented contribution is threefold; First, ternary color spaces are exploited to separate foreground from the background-utilizing multilevel approach of contrast stretching. Second, a weighting criterion is designed which is able to select the best solution based on extended texture feature analysis, related labels, boundary connections and central distance. Third, an improved feature extraction and dimensionality reduction criteria is proposed which combines conventional as well as recent feature extraction techniques. The proposed method is tested on PH2, ISBI 2016 and ISIC benchmark data sets and evaluated on the basis of multiple parameters including FPR, sensitivity, specificity, FNR and accuracy. From the statistics, it is quite clear that the proposed method outperforms numerous existing techniques with considerable margin.
A coarse-to-fine approach for segmenting melanocytic skin lesions in standard camera images
Computer Methods and Programs in Biomedicine, 2013
Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing step.
Automatic lesion segmentation for melanoma diagnostics in macroscopic images
2015 23rd European Signal Processing Conference (EUSIPCO), 2015
Detailed segmentation of pigmented skin lesions is an important requirement in computer aided applications for melanoma assessment. In particular, accurate segmentation is necessary for image-guided evaluation of skin lesions characteristics. In this paper, we present a new approach of histogram thresholding for detailed segmentation of skin lesions based on histogram analysis of the saturation color component in the hue-saturation-value (HSV) color space. The proposed technique is specifically developed with the aim to handle the complex variability of features for macroscopic color images taken in uncontrolled environment. A dataset of 30 cases with manual segmentation was used for evaluation. We compare our results with two of most important existing segmentation techniques. For similarity report between automatic and manual segmentation we used dice similarity coefficient (DSC), the true detection rate (TDR), and the false positive rate (FPR). Experimental results show that the proposed method has high precision and low computational complexity.
In this paper, we put forward a new pre-processing scheme for automatic analysis of dermoscopic images. Our contributions are two-fold. First, we present a procedure, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: these include shading induced by imaging non-flat skin surfaces and the effect of light-intensity falloff toward the edges of the dermoscopic image. This procedure is shown to facilitate the detection and removal of artifacts such as hairs as well. Second, we present a novel simple yet effective greyscale conversion approach that is based on physics and biology of human skin. Our proposed greyscale image provides high separability between a pigmented lesion and normal skin surrounding it. Finally, using our pre-processing scheme, we perform segmentation based on simple grey-level thresholding, with results outperforming the state of the art.
Colour and contrast enhancement for improved skin lesion segmentation
Computerized Medical Imaging and Graphics, 2011
Accurate extraction of lesion borders is a critical step in analysing dermoscopic skin lesion images. In this paper, we consider the problems of poor contrast and lack of colour calibration which are often encountered when analysing dermoscopy images. Different illumination or different devices will lead to different image colours of the same lesion and hence to difficulties in the segmentation stage. Similarly, low contrast makes accurate border detection difficult. We present an effective approach to improve the performance of lesion segmentation algorithms through a pre-processing step that enhances colour information and image contrast. We combine this enhancement stage with two different segmentation algorithms. One technique relies on analysis of the image background by iterative measurements of non-lesion pixels, while the other technique utilises co-operative neural networks for edge detection. Extensive experimental evaluation is carried out on a dataset of 100 dermoscopy images with known ground truths obtained from three expert dermatologists. The results show that both techniques are capable of providing good segmentation performance and that the colour enhancement step is indeed crucial as demonstrated by comparison with results obtained from the original RGB images.