2,654 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Accurate segmentation and registration of skin lesion images to evaluate lesion change
Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported
in part by the Spanish Government (HAVideo, TEC2014-53176-R) and
in part by the TEC department (Universidad Autonoma de Madrid
Gaussian mixture model based probabilistic modeling of images for medical image segmentation
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin
USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation
Unsupervised skin lesion segmentation offers several benefits, including
conserving expert human resources, reducing discrepancies due to subjective
human labeling, and adapting to novel environments. However, segmenting
dermoscopic images without manual labeling guidance presents significant
challenges due to dermoscopic image artifacts such as hair noise, blister
noise, and subtle edge differences. To address these challenges, we introduce
an innovative Uncertainty Self-Learning Network (USL-Net) designed for skin
lesion segmentation. The USL-Net can effectively segment a range of lesions,
eliminating the need for manual labeling guidance. Initially, features are
extracted using contrastive learning, followed by the generation of Class
Activation Maps (CAMs) as saliency maps using these features. The different CAM
locations correspond to the importance of the lesion region based on their
saliency. High-saliency regions in the map serve as pseudo-labels for lesion
regions while low-saliency regions represent the background. However,
intermediate regions can be hard to classify, often due to their proximity to
lesion edges or interference from hair or blisters. Rather than risk potential
pseudo-labeling errors or learning confusion by forcefully classifying these
regions, we consider them as uncertainty regions, exempting them from
pseudo-labeling and allowing the network to self-learn. Further, we employ
connectivity detection and centrality detection to refine foreground
pseudo-labels and reduce noise-induced errors. The application of cycle
refining enhances performance further. Our method underwent thorough
experimental validation on the ISIC-2017, ISIC-2018, and PH2 datasets,
demonstrating that its performance is on par with weakly supervised and
supervised methods, and exceeds that of other existing unsupervised methods.Comment: 14 pages, 9 figures, 71 reference
Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily
Segmentation and Classification of Skin Lesions for Disease Diagnosis
In this paper, a novel approach for automatic segmentation and classification
of skin lesions is proposed. Initially, skin images are filtered to remove
unwanted hairs and noise and then the segmentation process is carried out to
extract lesion areas. For segmentation, a region growing method is applied by
automatic initialization of seed points. The segmentation performance is
measured with different well known measures and the results are appreciable.
Subsequently, the extracted lesion areas are represented by color and texture
features. SVM and k-NN classifiers are used along with their fusion for the
classification using the extracted features. The performance of the system is
tested on our own dataset of 726 samples from 141 images consisting of 5
different classes of diseases. The results are very promising with 46.71% and
34% of F-measure using SVM and k-NN classifier respectively and with 61% of
F-measure for fusion of SVM and k-NN.Comment: 10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer
Science, International Conference on Advanced Computing Technologies and
Applications (ICACTA-2015
Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization
In this paper, we propose particle swarm optimization (PSO)-enhanced ensemble deep neural networks and hybrid clustering models for skin lesion segmentation. A PSO variant is proposed, which embeds diverse search actions including simulated annealing, levy flight, helix behavior, modified PSO, and differential evolution operations with spiral search coefficients. These search actions work in a cascade manner to not only equip each individual with different search operations throughout the search process but also assign distinctive search actions to different particles simultaneously in every single iteration. The proposed PSO variant is used to optimize the learning hyper-parameters of convolutional neural networks (CNNs) and the cluster centroids of classical Fuzzy C-Means clustering respectively to overcome performance barriers. Ensemble deep networks and hybrid clustering models are subsequently constructed based on the optimized CNN and hybrid clustering segmenters for lesion segmentation. We evaluate the proposed ensemble models using three skin lesion databases, i.e., PH2, ISIC 2017, and Dermofit Image Library, and a blood cancer data set, i.e., ALL-IDB2. The empirical results indicate that our models outperform other hybrid ensemble clustering models combined with advanced PSO variants, as well as state-of-the-art deep networks in the literature for diverse challenging image segmentation tasks
Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided
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