scholarly journals Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1224
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Skin Lesion ◽  
Random Field ◽  
Markov Random Field ◽  
Likelihood Function ◽  
Skin Lesions ◽  
Great Success ◽  
Region Merging ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Markov Random

Markov random field (MRF) theory has achieved great success in image segmentation. Researchers have developed various methods based on MRF theory to solve skin lesions segmentation problems such as pixel-based MRF model, stochastic region-merging approach, symmetric MRF model, etc. In this paper, the proposed method seeks to provide a complement to the advantages of the pixel-based MRF model and stochastic region-merging approach. This is in order to overcome shortcomings of the pixel-based MRF model, because of various challenges that affect the skin lesion segmentation results such as irregular and fuzzy border, noisy and artifacts presence, and low contrast between lesions. The strength of the proposed method lies in the aspect of combining the benefits of the pixel-based MRF model and the stochastic region-merging by decomposing the likelihood function into the multiplication of stochastic region-merging likelihood function and the pixel likelihood function. The proposed method was evaluated on bench marked available datasets, PH2 and ISIC. The proposed method achieves Dice coefficients of 89.65 % on PH2 and 88.34 % on ISIC datasets respectively.

scholarly journals Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

2020 ◽  
Vol 39 (3) ◽  
pp. 169-185
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Skin Lesions ◽  
Training Dataset ◽  
Great Success ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Low Contrast

Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

scholarly journals Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field

2021 ◽  
Vol 19 (2) ◽  
pp. 1891-1908
Author(s):  
Jianhua Song ◽  
◽  
Lei Yuan ◽  
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Brain Tissue ◽  
Markov Random Field ◽  
Tissue Segmentation ◽  
Low Contrast ◽  
Brain Tissue Segmentation ◽  
Markov Random ◽  
Fuzzy C Means Clustering ◽  
Non Local

<abstract> <p>The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.</p> </abstract>

Modeling Stereopsis via Markov Random Field

2010 ◽  
Vol 22 (8) ◽  
pp. 2161-2191 ◽  
Author(s):  
Yansheng Ming ◽  
Zhanyi Hu
Keyword(s):  
Random Field ◽  
Potential Function ◽  
Markov Random Field ◽  
Stereo Vision ◽  
Belief Propagation ◽  
Likelihood Function ◽  
Energy Model ◽  
Cooperative Interaction ◽  
Local Similarity ◽  
Markov Random

Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity encoders in the visual pathway. Our likelihood function is also relevant to several psychological findings. The potential function in our model is constrained by the psychological finding that the strength of the cooperative interaction minimizing relative disparity decreases as the separation between stimuli increases. Our model is tested on three kinds of stereo images. In simulations on images with repetitive patterns, we demonstrate that our model could account for the human depth percepts that were previously explained by the second-order mechanism. In simulations on random dot stereograms and natural scene images, we demonstrate that false matches introduced by the disparity energy model can be reliably removed using our model. A comparison with the coarse-to-fine model shows that our model is able to compute the absolute disparity of small objects with larger relative disparity. We also relate our model to several physiological findings. The hypothesized neurons of the model are selective for absolute disparity and have facilitative extra receptive field. There are plenty of such neurons in the visual cortex. In conclusion, we think that stereopsis can be implemented by neural networks resembling MRF.

scholarly journals Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

2015 ◽  
Vol 45 ◽  
pp. 102-111 ◽  
Author(s):  
Pallab Kanti Roy ◽  
Alauddin Bhuiyan ◽  
Andrew Janke ◽  
Patricia M. Desmond ◽  
Tien Yin Wong ◽  
...  
Keyword(s):  
White Matter ◽  
Random Field ◽  
Markov Random Field ◽  
White Matter Lesion ◽  
Lesion Segmentation ◽  
Markov Random ◽  
Contrast Enhanced

scholarly journals Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3462
Author(s):  
Shengxin Tao ◽  
Yun Jiang ◽  
Simin Cao ◽  
Chao Wu ◽  
Zeqi Ma
Keyword(s):  
Survival Rate ◽  
Skin Lesion ◽  
Automatic Segmentation ◽  
Skin Lesions ◽  
Diagnosis And Treatment ◽  
Guided Filter ◽  
Lesion Segmentation ◽  
Data Set ◽  
Low Contrast

The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.

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

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 72 ◽  
Author(s):  
Halil Murat Ünver ◽  
Enes Ayan
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
Critical Role ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Melanoma Detection ◽  
Sensitivity Rate ◽  
Dermoscopic Image ◽  
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.

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