Skin Image Segmentation Research Articles

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention.

Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.

Pact-Net: Parallel CNNs and Transformers for medical image segmentation

Background and ObjectiveThe image segmentation of diseases can help clinical diagnosis and treatment in medical image analysis. Because medical images usually have low contrast and large changes in the size and shape of some structures, this will lead to over-segmentation and under-segmentation. These problems are particularly evident in the segmentation of skin damage. The blurring of the boundary in skin images and the specificity of patients will further increase the difficulty of skin lesion segmentation. Currently, most researchers use deep learning networks to solve these skin segmentation problems. However, traditional convolution methods often fail to obtain satisfactory segmentation performance due to their shortcomings in obtaining global features. Recently, Transformers with good global information extraction ability has achieved satisfactory results in computer vision, which brings new solutions to optimize the model of medical image segmentation further. MethodsTo extract more features related to medical image segmentation and effectively use features to further optimize the skin image segmentation model, we designed a network that combines CNNs and Transformers to improve local and global features, called Parallel CNNs and Transformers for Medical Image Segmentation (Pact-Net). Specifically, due to the advantages of Transformers in extracting global information, we create a novel fusion module CSMF, which uses channel and spatial attention mechanism and multi-scale mechanism to effectively fuse the global information extracted by Transformers into the local features extracted by CNNs. Therefore, our Pact-Net dual-branch runs in parallel to effectively capture global and local information. ResultsOur Pact-Net exceeds the models submitted on the three datasets ISIC 2016, ISIC 2017 and ISIC 2018, and the indicators required for the datasets reach 86.95%, 79.31% and 84.14%, respectively. We also conducted medical image segmentation experiments on cell and polyp datasets to evaluate the robustness, learning and generalization ability of the network. The ablation study of each part of Pact-Net proves the validity of each component, and the comparison with state-of-the-art methods on different indicators proves the predominance of the network. ConclusionsThis paper uses the advantages of CNNs and Transformers in extracting local and global features, and further integrates features for skin lesion segmentation. Compared with the state-of-the-art methods, Pact-Net can achieve the most advanced segmentation ability on the skin lesion segmentation dataset, which can help doctors diagnose and treat diseases.

BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation.

Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively.

Skin lesion segmentation of dermoscopy images using U-Net

Skin cancer is one of the most threatening cancers as reported and has been on the increase over the past 10 years. The traditional methods of skin cancer segmentation are time-consuming and inefficient. U-Net is a powerful and accurate way of self-segmentation in the medical field. In order to solve this problem, this paper proposes a U-Net skin cancer segmentation system that can provide results and feedback quickly, accurately and intelligently. It is composed of two parts: Skin Image Analysis Module and Skin Image Segmentation Module. In the skin image analysis module, the system learns segmentation from the training set images, and verifies the correctness of learning from part of the images. In Skin Image Segmentation Module, the system segments all the images in the test set folder. Among several experiments, the system using GPU training and learning with 100 images of ISIC dataset after 10 epochs has the training accuracy of 0.9085, while the validation accuracy is 0.9536. The system allows users to upload their lesion images to a test folder to obtain reliable segmentation results in a timely manner, thereby improving the survival rate of potential patients.

Convolutional neural network-based skin image segmentation model to improve classification of skin diseases in conventional and non-standardized picture images

BackgroundFor dermatological practices, non-standardized conventional photo images are taken and collected as a mixture of variable fields of the image view, including close-up images focusing on designated lesions and long-shot images including normal skin and background of the body surface. Computer-aided detection/diagnosis (CAD) models trained using non-standardized conventional photo images exhibit lower performance rates than CAD models that detect lesions in a localized small area, such as dermoscopic images. ObjectiveWe aimed to develop a convolutional neural network (CNN) model for skin image segmentation to generate a skin disease image dataset suitable for CAD of multiple skin disease classification. MethodsWe trained a DeepLabv3 + -based CNN segmentation model to detect skin and lesion areas and segmented out areas that satisfy the following conditions: more than 80% of the image will be the skin area, and more than 10% of the image will be the lesion area. ResultsThe generated CNN-segmented image database was examined using CAD of skin disease classification and achieved approximately 90% sensitivity and specificity to differentiate atopic dermatitis from malignant diseases and complications, such as mycosis fungoides, impetigo, and herpesvirus infection. The accuracy of skin disease classification in the CNN-segmented image dataset was almost equal to that of the manually cropped image dataset and higher than that of the original image dataset. ConclusionOur CNN segmentation model, which automatically extracts lesions and segmented images of the skin area regardless of image fields, will reduce the burden of physician annotation and improve CAD performance.

Cascaded Hough Transform-Based Hair Mask Generation and Harmonic Inpainting for Automated Hair Removal from Dermoscopy Images.

Restoring information obstructed by hair is one of the main issues for the accurate analysis and segmentation of skin images. For retrieving pixels obstructed by hair, the proposed system converts dermoscopy images into the L*a*b* color space, then principal component analysis (PCA) is applied to produce grayscale images. Afterward, the contrast-limited adaptive histogram equalization (CLAHE) and the average filter are implemented to enhance the grayscale image. Subsequently, the binary image is generated using the iterative thresholding method. After that, the Hough transform (HT) is applied to each image block to generate the hair mask. Finally, the hair pixels are removed by harmonic inpainting. The performance of the proposed automated hair removal was evaluated by applying the proposed system to the International Skin Imaging Collaboration (ISIC) dermoscopy dataset as well as to clinical images. Six performance evaluation metrics were measured, namely the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), the signal-to-noise ratio (SNR), the structural similarity index (SSIM), the universal quality image index (UQI), and the correlation (C). Using the clinical dataset, the system achieved MSE, PSNR, SNR, SSIM, UQI, and C values of 34.7957, 66.98, 42.39, 0.9813, 0.9801, and 0.9985, respectively. The results demonstrated that the proposed system could satisfy the medical diagnostic requirements and achieve the best performance compared to the state-of-art.

Review: a comparative study of state-of-the-art skin image segmentation techniques with CNN

Review: a comparative study of state-of-the-art skin image segmentation techniques with CNN

NURBS functional network approach for automatic image segmentation of macroscopic medical images in melanoma detection

Image processing techniques are becoming standard technology in many medical specialities, such as dermatology, where they are a key tool for the early detection and diagnosis of melanoma and other skin cancers and tumors. A previous paper by the authors presented at SOCO 2020 conference introduced a new method for image segmentation of skin images through functional networks. The method performs well but it relies on a semi-automatic approach involving a combination of manual and automatic operations. This paper aims at making image segmentation of macroscopic skin images a fully automatic process. To this purpose, the present work extends our previous paper with five new relevant contributions: (1) a filtering strategy for removal of noise, hair and other artifacts; (2) two morphological operators for image enhancement; (3) a clustering-based binary classifier to separate the skin tumor from the image background; (4) a smoothing and discretization process to obtain the border points; and (5) a curve reconstruction method from the border points with NURBS using a new type of functional network particularly tailored for this task. This new method is applied to two different benchmarks, comprised respectively of four and two macroscopic medical images of skin tumors. The visual and numerical results show that the method performs very well, yielding segmented images which are suitable for clinical practice. This method is a significant step towards the future development of a fully automatic approach for the whole medical image analysis pipeline of skin images, including diagnosis and classification.

ResBCU-Net: Deep learning approach for segmentation of skin images

Large networks on big datasets have put great impact in success of deep neural networks (DNNs). The greatest breakthrough achieved in this field in 2012 by successful training of a large network, AlexNet, on ImageNet dataset containing 1 million images. U-Net architecture, a DNN based on Convolutional Neural Networks (CNNs), immensely advanced segmentation of medical images. In the present times, neural networks have outperformed other state-of-the-art approaches in segmentation of images. In this paper, we present a neural network based on the CNNs for segmentation of medical images. The network, ResBCU-Net, is an extension of the U-Net which utilizes Residual blocks, Batch normalization and Bi-directional ConvLSTM. In addition, we present an extended form of ResBCU-Net, ResBCU-Net(d = 3), which takes advantage of densely connected layers in its bottleneck section. The proposed neural network is trained and evaluated on ISIC 2018 dataset, which is publicly available dataset containing 2594 melanoma cancerous skin images. The network inferences segmentation of the images more accurately than other state-of-the-art alternatives.

Skin lesion classification using decision trees and random forest algorithms

Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images.

Contour with Connected Components and Mesh Surface for Skin Image Segmentation

Contour with Connected Components and Mesh Surface for Skin Image Segmentation

Skin Images Segmentation

Problem statement: Image segmentation is a fundamental step in many a pplications of image processing. Skin cancer has been the most common of all new cancers detected each year. At early stage detection of skin cancer, simple and ec onomic treatment can cure it mostly. An accurate segmentation of skin images can help the diagnosis to define well the region of the cancer. The principal approach of segmentation is based on thre sholding (classification) that is lied to the probl em of the thresholds estimation. Approach: The objective of this study is to develop a method to segment the skin images based on a mixture of Beta distribu tions. We assume that the data in skin images can be modeled by a mixture of Beta distributions. We u sed an unsupervised learning technique with Beta distribution to estimate the statistical parameters of the data in skin image and then estimate the thresholds for segmentation. Results: The proposed method of skin images segmentation was implemented and tested on different skin images. We obtained very good results in comparing with the same techniques with Gamma distribution. Conclusion: The experiment showed that the proposed method obtained very good results but it requires m ore testing on different types of skin images.

Skin image segmentation based on energy transformation.

In this paper, a new method on extraction of human skin grid centerlines is proposed. The method introduces the physics concepts of kinetic and potential energy into image processing. Regional energy is calculated. Energy transformation is performed to map the pixels from the grayscale space into energy space. Then, the energy image undergoes a morphological filter to remove noises and spurious minima. The amount of filtering can be manually tuned to get a different result. Subsequently, normal curvature of the energy surface is utilized to identify the principal direction and principal curvatures. The ridge centerlines can be detected at the image locations where the principal direction is perpendicular to the normal vector. The experiment shows that this method is an effective one for the purpose of extracting human skin grid.

Skin Image Segmentation Using a Self-Organizing Map and Genetic Algorithms

Skin Image Segmentation Using a Self-Organizing Map and Genetic Algorithms