Skin Lesions In Dermoscopic Images Research Articles

Lightweight encoder-decoder model for automatic skin lesion segmentation

Accurate skin lesion segmentation (SLS) is an important step in computer-aided diagnosis of melanoma. Automatic detection of skin lesions in dermoscopy images is challenging because of the presence of artifacts and as lesions can have heterogeneous texture, color, and shape with fuzzy or indistinct boundaries. In this study, automatic SLS was performed using a lightweight encoder-decoder, MobileNetV3-UNet, which can achieve high accuracy with low resources. A comprehensive analysis was performed to improve the accuracy of the method in SLS. The semantic segmentation method consists of an encoder-decoder architecture, data augmentation, learning schemes, and post-processing methods. To enhance the SLS, we modified the decoder with the bidirectional ConvLSTM layer from the BCDU-Net and separable blocks from the separable-UNet architecture. Random augmentation was used to improve image diversity in the training dataset to avoid overfitting. Furthermore, a learning scheme based on stochastic weight averaging (SWA) was used to obtain better generalization by averaging multiple local optima. Our method was evaluated using three publicly available datasets, such as ISIC-2017, ISIC-2018, and PH2. We obtained dice coefficient and Jaccard index of 87.74%, 80.25%; 91.01%, 83.44%; and 95.18%, 91.08% for ISIC-2017, ISIC-2018, and PH2, respectively. The experimental results proved that the modified MobileNetV3-UNet method can outperform several state-of-the-art methods.

Drop dermoscopy for teledermatology

There has been an explosion in the practice of teledermatology recently because of the COVID-19 pandemic. This includes patient-initiated consultations. One of the main limitations of patient-initiated consultations is the difficulty of incorporating dermoscopy. A drop of clear alcohol-based sanitizer is applied over the lesion to be examined, and the patient (or a helper) takes a photograph of the lesion through the fluid. The fluid reduces the reflection from the surface and helps visualization of the subsurface structures (Figs 1 and 2). Any smartphone or point-and-shoot camera is good enough for the imaging, making it easy to transfer the image to the dermatologist. The quality and resolution are better when dedicated cameras with macro mode capabilities are used.Fig 2A, Small cherry angioma: clinical image. B, Image taken after applying alcohol solution.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Blum and Giacomel1Blum A. Giacomel J. “Tape dermatoscopy”: constructing a low-cost dermatoscope using a mobile phone, immersion fluid and transparent adhesive tape.Dermatol Pract Concept. 2015; 5: 87-93PubMed Google Scholar have previously described a concept called tape dermoscopy, in which smartphones are used to take dermoscopic images of skin lesions after the immersion fluid is applied, followed by covering of the area with a transparent adhesive tape. The method we describe is simpler and faster, and it avoids the practical difficulties of having to apply adhesive tape over a liquid surface. The main limitations in our method are the minimal magnification (although the inherent zoom of the capture device can be used) and the reflection of the flash interfering with the image quality. The reflection can be reduced by trying out different angles while taking the image. Although the quality of the dermoscopy is obviously not comparable to that of standard dermoscopes, this can be an easy-to-use, simple screening and triage tool in the context of patient-initiated teledermatology.

Real-Time Mobile-Phone-Aided Melanoma Skin Lesion Detection Using Triangulation Technique

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas to improve the patient prognosis. A biopsy is a traditional method which is painful and invasive when used for skin cancer detection. This method requires laboratory testing which is not very efficient and time-consuming to detect skin lesions. To solve the above issue, a computer aided diagnosis (CAD) for skin lesion detection is needed. In this article, we have developed a mobile application with the capabilities to segment skin lesions in dermoscopy images using a triangulation method and categorize them into malignant or bengin lesions through a supervised method which is convolution neural network (CNN). This mobile application will make the skin cancer detection non-invasive which does not require any laboratory testing, making the detection less time consuming and inexpensive with a detection accuracy of 81%.

Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review

Automated intelligent systems for unbiased diagnosis are primary requirement for the pigment lesion analysis. It has gained the attention of researchers in the last few decades. These systems involve multiple phases such as pre-processing, feature extraction, segmentation, classification and post processing. It is crucial to accurately localize and segment the skin lesion. It is observed that recent enhancements in machine learning algorithms and dermoscopic techniques reduced the misclassification rate therefore, the focus towards computer aided systems increased exponentially in recent years. Computer aided diagnostic systems are reliable source for dermatologists to analyze the type of cancer, but it is widely acknowledged that even higher accuracy is needed for computer aided diagnostic systems to be adopted practically in the diagnostic process of life threatening diseases. Skin cancer is one of the most threatening cancers. It occurs by the abnormal multiplication of cells. The core three types of skin cells are: Squamous, Basal and Melanocytes. There are two wide classes of skin cancer; Melanocytic and non-Melanocytic. It is difficult to differentiate between benign and malignant melanoma, therefore dermatologists sometimes misclassify the benign and malignant melanoma. Melanoma is estimated as 19th most frequent cancer, it is riskier than the Basel and Squamous carcinoma because it rapidly spreads throughout the body. Hence, to lower the death risk, it is critical to diagnose the correct type of cancer in early rudimentary phases. It can occur on any part of body, but it has higher probability to occur on chest, back and legs. The paper presents a review of segmentation and classification techniques for skin lesion detection. Dermoscopy and its features are discussed briefly. After that Image pre-processing techniques are described. A thorough review of segmentation and classification phases of skin lesion detection using deep learning techniques is presented Literature is discussed and a comparative analysis of discussed methods is presented. In this paper, we have presented the survey of more than 100 papers and comparative analysis of state of the art techniques, model and methodologies. Malignant melanoma is one of the most threating and deadliest cancers. Since the last few decades, researchers are putting extra attention and effort in accurate diagnosis of melanoma. The main challenges of dermoscopic skin lesion images are: low contrasts, multiple lesions, irregular and fuzzy borders, blood vessels, regression, hairs, bubbles, variegated coloring and other kinds of distortions. The lack of large training dataset makes these problems even more challenging. Due to recent advancement in the paradigm of deep learning, and specially the outstanding performance in medical imaging, it has become important to review the deep learning algorithms performance in skin lesion segmentation. Here, we have discussed the results of different techniques on the basis of different evaluation parameters such as Jaccard coefficient, sensitivity, specificity and accuracy. And the paper listed down the major achievements in this domain with the detailed discussion of the techniques. In future, it is expected to improve results by utilizing the capabilities of deep learning frameworks with other pre and post processing techniques so reliable and accurate diagnostic systems can be built.

Real-world outcomes of melanoma surveillance using the MoleMap NZ telemedicine platform

MoleMap NZ is a novel New Zealand-based store-and-forward telemedicine service to detect melanoma. It uses expert review of total body photography and close-up and dermoscopic images of skin lesions that are suspicious for malignancy. The purpose of this study was to assess the effectiveness of MoleMap NZ as a melanoma early detection program. We conducted a review of 2108 melanocytic lesions recommended for biopsy/excision by MoleMap NZ dermoscopists between January 2015 and December 2016. Pathologic diagnoses were available for 1571 lesions. Of these, 1303 (83%) lesions were benign and 260 (17%) lesions were diagnosed as melanoma, for a melanoma-specific benign:malignant ratio of 5.0:1. The number needed to biopsy to obtain 1 melanoma was 6. Among melanomas with available tumor thickness data (n=137), 92% were <0.8mm (range in situ to 3.1mm), with in situ melanomas comprising 74%. Only lesions recommended for excision were analyzed. Pathology results were available for 75% of these cases. Tumor thickness data were available for 53% of melanomas diagnosed. This real-world study of MoleMap NZ, a community-based teledermoscopy program, suggests that it has the potential to increase patients' access to specialist expertise via telemedicine. Additional studies are needed to more accurately define its efficacy.

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and short-cut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyper-parameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.

Skin Lesion Detection and Segmentation using SVM and Bayesian Classifier

Skin cancer is a peculiar growth of skin cells; often develops on skin exposed to the sun. Skin cancer evolves predominantly on areas of sun-exhibited skin includes scalp, face, lips, ears, neck, chest, arm, hands and the legs in human. But it may appear on the areas that seldom see the sunshine of day like palms, at a lower place fingernails or toenails and reproductive organ space. However, it is curable if it gets detected at an early stage. To minimize the diagnostic error caused by the complexity of visual interpretation and subjectivity, it is important to develop a technology like computerized image analysis. This article presents a systematic approach for the classification of skin lesions in dermoscopic images. For detecting the melanoma skin cancer, the Otsu algorithm is applied to segment the lesion from the entire image. In the proposed method, feature extraction is performed by underlying shape, color, texture using GLCM (Gray-Level Co-Occurrence Matrix) features which are applied with support vector machine and Bayesian classifiers. While comparing, the Bayesian classifier achieved an overall classification accuracy of 90.44% on a dataset of 4151 images which is better than the existing method.

Multi-Class Skin Lesions Classification System Using Probability Map Based Region Growing and DCNN

BackgroundMelanoma is a type of threatening pigmented skin lesion, and as of now is among the most hazardous existing diseases. Suitable automated diagnosis of skin lesions and Melanoma classification can extraordinarily enhance early identification of Melanomas.MethodsHowever, classification models based on deterministic skin lesion can influence multi-dimensional nonlinear problem which leads to inaccurate and inefficient classification. This paper presents a Deep Convolutional Neural Network (DCNN) classification approach for segmented skin lesions in dermoscopy images. As an initial step, the skin lesion is preprocessed by an automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also a new probability map based region growing and optimal thresholding algorithm is integrated in our system which yields tremendous accuracy.ResultsFor obtaining more prominent results a set of features containing ABCD features as well as geometric features are calculated in the feature extraction step to describe the malignancy of the lesion.ConclusionsThe experimental result shows that the system is efficient and works well on dermoscopy images, achieving considerable accuracy.

Automatic Focus Assessment on Dermoscopic Images Acquired with Smartphones.

Over recent years, there has been an increase in popularity of the acquisition of dermoscopic skin lesion images using mobile devices, more specifically using the smartphone camera. The demand for self-care and telemedicine solutions requires suitable methods to guide and evaluate the acquired images’ quality in order to improve the monitoring of skin lesions. In this work, a system for automated focus assessment of dermoscopic images was developed using a feature-based machine learning approach. The system was designed to guide the user throughout the acquisition process by means of a preview image validation approach that included artifact detection and focus validation, followed by the image quality assessment of the acquired picture. This paper also introduces two different datasets, dermoscopic skin lesions and artifacts, which were collected using different mobile devices to develop and test the system. The best model for automatic preview assessment attained an overall accuracy of 77.9% while focus assessment of the acquired picture reached a global accuracy of 86.2%. These findings were validated by implementing the proposed methodology within an android application, demonstrating promising results as well as the viability of the proposed solution in a real life scenario.

Classification of Skin Lesions in Dermatoscopic Images with Deep Convolution Network

In this study, a deep learning based solution with convolutional neural network is presented to solve the problem of classification of dermoscopic images containing skin lesions. Defining models with classical machine learning techniques takes a lot of time and with this model, it cannot make data meaningful without pretreatment. Thanks to deep learning, we have come a long way in problems that we think are difficult to solve for many years. Deep learning achieves results by processing the data at hand without any intervention by us. Classification of dermoscopic images of skin lesions is a difficult task to distinguish between benign or malignant melanocytic tumors. Malignant melanoma is the deadliest form of skin cancer and is one of the fastest developing cancers in the world. If diagnosed early, it can be easily treated and ultimately, early diagnosis of melanoma is vital. Dermoscopy has become one of the most important tools in the diagnosis of melanoma and other pigmented skin lesions. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgment, there has been a need to process the dermoscopy image with an automatic recognition algorithm. In 2017, the support vector machine (SVM) classifier was used to differentiate 172 dermatoscopic images into two classes as “benign”and “malignant”. Experiments on the dataset have 91% accuracy. However, the fact that we have thousands of images in our data set and that we will break them down into seven lesion classes required us to search for more effective methods. Classroom inconsistency of melanomas is considered a challenging process due to the low contrast of skin lesions and artificial objects such as noise, presence of hair, air bubbles and similarity between non-melanoma cases in dermoscopy images. To solve these problems, we propose the VGGNET-16 architecture, which includes a powerful convolutional neural network model to classify seven different types of disease on dermoscopic images. “HAM10000 ”(Human Againist Machine) data set was used with VGGNET-16 architecture and the results were observed. The data set, which is published as an educational set for academic machine learning and made public through the ISIC archive, consists of 10015 dermatoscopic images. K-Fold Cross Validation technique was used to differentiate the data set consisting of seven lesion classes as training and test area. In the test phase of the educated model, the validation of the classes was obtained as 85.62%.

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm.

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

Artificial Intelligence in Skin Cancer

To review recent developments in artificial intelligence for skin cancer diagnosis. Major breakthroughs in recent years are likely related to advancements in utilization of convolutional neural networks (CNNs) for dermatologic image analysis, especially dermoscopy. Recent studies have shown that CNN-based approaches perform as well as or even better than human raters in diagnosing close-up and dermoscopic images of skin lesions in a simulated static environment. Several limitations for the development of AI include the need for large data pipelines and ground truth diagnoses, lack of metadata, and lack of rigorous widely accepted standards. Despite recent breakthroughs, adoption of AI in clinical settings for dermatology is in early stages. Close collaboration between researchers and clinicians may provide the opportunity to investigate implementation of AI in clinical settings to provide real benefit for both clinicians and patients.

A comparative study of deep learning architectures on melanoma detection

Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However, some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing unit (GPU) to speed up the training and deployment process. To enhance the quality of images, we employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation could help to improve the final accuracy.

Attention-Based DenseUnet Network With Adversarial Training for Skin Lesion Segmentation

Automatic segmentation of skin lesions in dermoscopy images is a challenging task due to the large size and shape variations of the lesions, the existence of various artifacts, the low contrast between the lesion and the surrounding skin. In this paper, we propose a novel Attention Based DenseUnet network (referred as Att-DenseUnet) with adversarial training for skin lesion segmentation. Att-DenseUnet is a Generative Adversarial Network which contains two major components: Segmentor and Discriminator. In the Segmentor module, we propose an architecture which is similar to DenseNet in the down-sampling path to ensure maximum multi-scale skin lesions information transfer between layers in the network at dense scale range, meanwhile, we design an attention module to automatically focus on the skin lesion features and suppress the irrelevant artifacts features in the output feature maps of the DenseBlocks. In the Discriminator module, we employ adversarial feature matching loss to train the Segmentor stably, force the Segmentor to extract multi-scale discriminative features, and guide the attention module focusing on the multi-scale skin lesions. A novel loss function of the Segmentor is proposed which combines the jaccard distance loss with the adversarial feature matching loss introduced by the Discriminator. We trained the proposed Att-DenseUnet on ISBI2017 dataset. The test results show that our approach gains the state-of-the-art performance, especially for JAC (0.8045) and SEN (0.8734) scores which are significantly improved by 2.2% and 1.9%, respectively, also our network is robust to different datasets, and gains the lowest time cost which make our network suitable for clinical application.

Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding

Background and objective: To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem.Methods: The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding.Results: This method participated in the 2016 and 2017 ISBI (International Symposium on Biomedical Imaging) Challenges, hosted by the ISIC (International Skin Imaging Collaboration). It was tested against two public databases containing 379 and 600 images respectively, and compared using the same defined metrics (Accuracy, Dice Coefficient, Jaccard Index, Sensitivity and Specificity) with the rest of participating state-of-the-art work, obtaining good results: (0.934, 0.869, 0.791, 0.870 and 0.978) and (0.884, 0.760, 0.665, 0.869 and 0.923) respectively, ranking 9th and 15th out of a total of 21 and 28 participants respectively using the Jaccard Index (which was the indicator used as a basis for ranking) and the 1st in the 2017 Challenge using the Sensitivity.Conclusion: The method has been proven to be robust and reliable. It’s main contribution is the very design of the algorithm, highly innovative, which could also be used to deal with other segmentation problems of a similar nature.

Fusing fine-tuned deep features for skin lesion classification

Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images.

Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters

In this work, we focus in the analysis of dermoscopy images using convolutional neural networks (CNNs). More specifically, we investigate the value of augmenting CNN inputs with the response of mid-level computer vision filters, using the traditional inputting of simple RGB pixel values as baseline. The proposed methodology is applied on two pattern recognition problems with clinical significance: the binary classification of skin lesions in dermoscopy images into “malignant” and “non-malignant” (nevus skin lesions) cases and the four-class, superpixel classification into differential structures that appear in skin lesions. The transfer learning technique is also utilized to compensate for the limited size of the available training image datasets. Results show that filter-based input augmentation using the response of mid-level computer vision filters significantly improves the classification accuracy achieved by the CNN architectures and simplifies the weights of the receptive fields.

Characteristic analysis of sebaceous nevus using dermoscopy and reflectance confocal microscopy

Objective To investigate dermoscopic and reflectance confocal microscopic (RCM) features of sebaceous nevus in patients of different ages. Methods A total of 83 patients histopathologically diagnosed with sebaceous nevus were enrolled from Department of Dermatology of Wuhan No. 1 Hospital between January 2016 and December 2016, and divided into 4 age groups (< 10 years, 10 - 20 years, 21 - 59 years, ≥ 60 years) according to the age. The dermoscopic and RCM features were collected and analyzed. Results Among the age group of < 10 years (21 cases) , dermoscopic images of skin lesions showed that uniform, globular or plaque-shaped yellowish-red structures arranged in a cobblestone-like pattern were independently distributed in an orange background, with vascular proliferation and dilatation, while RCM images showed sebaceous gland hypoplasia and juvenile hair follicles. Among the age group of 10 - 20 years (28 cases) , dermoscopy showed aggregated, non-hair follicle, round or oval yellow structures of varying sizes with telangiectases, while RCM showed botryoidal sebaceous glands at the dermal-epidermal junction and superficial dermis, which were characterized by tube-like or handle-like structure in the center, aggregated fish roe-like or frogspawn-like sebaceous gland lobules in the surrounding area and verrucous or papillomatous hyperplasia in the epidermis. The age groups of 21 - 59 years (30 cases) and 10 - 20 years showed the similar imaging manifestations. The main imaging manifestation of the age group of ≥ 60 years (4 cases) was papillomatous hyperplasia. Conclusions Sebaceous nevus is a dynamic developmental disease, and its imaging characteristics obviously differ among different age stages. Sebaceous nevus has characteristic structures under dermoscopy and RCM, which can serve as efficient noninvasive diagnostic methods for sebaceous nevus. Key words: Sebaceous gland neoplasms; Microscopy, confocal; Dermoscopy; Sebaceous nevus

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Background and objectiveAutomatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma. MethodsIn this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN). The proposed FrCN method directly learns the full resolution features of each individual pixel of the input data without the need for pre- or post-processing operations such as artifact removal, low contrast adjustment, or further enhancement of the segmented skin lesion boundaries. We evaluated the proposed method using two publicly available databases, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets. To evaluate the proposed method, we compared the segmentation performance with the latest deep learning segmentation approaches such as the fully convolutional network (FCN), U-Net, and SegNet. ResultsOur results showed that the proposed FrCN method segmented the skin lesions with an average Jaccard index of 77.11% and an overall segmentation accuracy of 94.03% for the ISBI 2017 test dataset and 84.79% and 95.08%, respectively, for the PH2 dataset. In comparison to FCN, U-Net, and SegNet, the proposed FrCN outperformed them by 4.94%, 15.47%, and 7.48% for the Jaccard index and 1.31%, 3.89%, and 2.27% for the segmentation accuracy, respectively. Furthermore, the proposed FrCN achieved a segmentation accuracy of 95.62% for some representative clinical benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases in the ISBI 2017 test dataset, exhibiting better performance than those of FCN, U-Net, and SegNet. ConclusionsWe conclude that using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.

Active Contours Based Segmentation and Lesion Periphery Analysis For Characterization of Skin Lesions in Dermoscopy Images.

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.