SDI+: A Novel Algorithm for Segmenting Dermoscopic Images (original) (raw)
Related papers
arXiv (Cornell University), 2017
We propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentation. We present extensive experimental results achieved by the SDI algorithm on the lesion segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection, highlighting its advantages and disadvantages.
DSNet: Automatic dermoscopic skin lesion segmentation
Computers in Biology and Medicine, 2020
Background and Objective: Automatic segmentation of skin lesions is considered a crucial step in Computeraided Diagnosis (CAD) systems for melanoma detection. Despite its significance, skin lesion segmentation remains an unsolved challenge due to their variability in color, texture, and shapes and indistinguishable boundaries. Methods: Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used a depth-wise separable convolution in lieu of standard convolution to project the learned discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented both a U-Net and a Fully Convolutional Network (FCN8s) to compare against the proposed DSNet. Results: We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 1 and PH2 2. The obtained mean Intersection over Union (mIoU) is 77.5% and 87.0% respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0% with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6% and 6.8% with respect to mIoU on the ISIC-2017 dataset. Conclusion: Our network for skin lesion segmentation outperforms the other methods discussed in the article and is able to provide better-segmented masks on two different test datasets which can lead to better performance in melanoma detection. Our trained model along with the source code and predicted masks are made publicly available 3 .
Preliminary work on dermatoscopic lesion segmentation
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012
Dermoscopy has become the primary tool used for pigmented skin lesion diagnosis providing better quality and accurate images. Computer-Assisted Image Interpretation is a new direction that involves the automatical lesion detection, feature extraction and classification (benign or malignant). This paper refers to several prior pre-processing enhancement techniques and an automated segmentation method. We have tested our methods on 60 dermoscopic images and compared the automated segmentation results with dermatologist-determined segmentation using an area percentage error.
A wide spread of algorithms for automatic segmentation of dermoscopic images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013
Currently, there is a great interest in the development of computer-aided diagnosis (CAD) systems for dermoscopic images. The segmentation step is one of the most important ones, since its accuracy determines the eventual success or failure of a CAD system. In this paper, different kinds of algorithms for the automatic segmentation of skin lesions in dermoscopic images were implemented and evaluated, namely automatic thresholding, k-means, mean-shift, region growing, gradient vector flow (GVF), and watershed. The segmentation methods were evaluated with three distinct metrics, using as ground truth a database of 50 images manually segmented by an expert dermatologist. Among the implemented segmentation approaches, the GVF snake method achieved the best segmentation performance.
Two-stage Skin Lesion Segmentation from Dermoscopic Images by Using Deep Neural Networks
2020
DOI: 10.29252/jorjanibiomedj.8.2.58 Abstract Background and objective: Automatic semantic segmentation of skin lesions is one of the most important medical requirements in the diagnosis and treatment of skin cancer, and scientists always try to achieve more accurate lesion segmentation systems. Developing an accurate model for lesion segmentation helps in timely diagnosis and appropriate treatment.
Skin Lesion Segmentation in Dermoscopy Imagery
The International Arab Journal of Information Technology, 2022
The main purpose of this study is to find an optimum method for segmentation of skin lesion images. In the present world, Skin cancer has proved to be the most deadly disease. The present research paper has developed a model which encompasses two gradations, the first being pre-processing for the reduction of unwanted artefacts like hair, illumination or many other by enhanced technique using threshold and morphological operations to attain higher accuracy and the second being segmentation by using k-mean with optimized Firefly Algorithm (FFA) technique. The online image database from the International Skin Imaging Collaboration (ISIC) archive dataset and dermatology service of Hospital Pedro Hispano (PH2) dataset has been used for input sample images. The parameters on which the proposed method is measured are sensitivity, specificity, dice coefficient, jacquard index, execution time, accuracy, error rate. From the results, authors have observed proposed model gives the average acc...
Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network
Sensors
Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architec...
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.
2021
Background: Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy and histopatholog-ical analysis are common procedures for skin cancer detection and prevention in clinical settings. A significant step involved in the diagnosis process is the deep understanding of patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual seg-mentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time which makes its prediction challenging. Moreover, at the initial stage, it is difficult to predict melanoma as it closely resembles other skin cancer types that are not malignant as melanoma, thus automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. Methods: As deep learning approaches have gained high attention in recent years due to their remarka...
The effects of skin lesion segmentation on the performance of dermatoscopic image classification
Computer Methods and Programs in Biomedicine, 2020
Background and Objective Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question. Methods In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. The different scenarios included approaches that exploited the segmentation masks either for cropping of skin lesion images or removing the surrounding background or using the segmentation masks as an additional input channel for model training. Results Evaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks. Conclusions We systematically explored the effects of using image segmentation on the performance of dermatoscopic skin lesion classification.