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

Salih, Omran; Viriri, Serestina

doi:10.3390/sym12081224

Cited by 23 publications

(19 citation statements)

References 30 publications

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“…LIN (Li and Shen, 2018) 95.0 75.3 83.9 biDFL (Wang et al, 2019) 94.65 81.47 88.54 SLS 94.31 79.26 86.93 FCNs (Zhang et al, 2019) 92.73 72.94 81.81 DCEDN (Adegun and Viriri, 2019a) (Bi et al, 2017) 94.24 83.99 90.66 FrCN (Al-Masni et al, 2018) 95.08 84.79 91.77 Ensemble (Goyal et al, 2020) 93.80 83.96 90.70 SRMP (Salih and Viriri, 2020) 91 The Local binary convolutional-deconvolutional approach overcomes the limitation of deep convolutional networks in producing coarsely segmented outputs when processing challenging skin lesion images. In this approach, the whole network is divided into stages, with each stage handling a section of the segmentation process.…”

Section: Methods Acc Ji Dicementioning

confidence: 99%

“…For instance, some of the pitfalls include: the pixel-based MRF model which can use the macro texture pattern description to have interactions in a large neighborhood, the stochastic regionmerging approach use the regular structural context to capture the fundamental relationship between regions more efficiently. For the reasons above, MRF-based techniques work combined the benefits of two or three models based MRF in one model (Salih and Viriri, 2018a;Salih et al, 2019;Salih and Viriri, 2020).…”

Section: Related Workmentioning

confidence: 99%

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Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Salih

Viriri

2020

Image Anal Stereol

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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.

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Section: Methods Acc Ji Dicementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Salih

Viriri

2020

Image Anal Stereol

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“…Phan et al [ 25 ] proposed an adjustable skip connection, which solves the problem of large scale variation among layers by performing an adjustable skip connection operation through a selective kernel module. Salih et al [ 26 ] decomposed the likelihood function, which more effectively gave play to the advantages of the combination of the pixel-based MRF model and random region. Khan et al [ 27 ] used local color-controlled histogram intensity values (LCcHIV) to enhance the input image to enrich the information.…”

Section: Related Workmentioning

confidence: 99%

FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Dong

Wang

Cheng

et al. 2021

Sensors

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Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

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“…However, the diagnostic accuracy of dermoscopy is heavily dependent on the experience of a dermatologist and visual assessment is highly onerous, subjective, and non-productive because of the complex nature of dermoscopic images [ 4 ]. These intrinsic curbs can be mitigated with the help of a computerized dermoscopic analysis system that allows for fast and accurate decisions in detecting skin lesions [ 4 , 9 , 10 ]. Computer-assisted diagnosis is of paramount importance to increase the accuracy and efficiency of the diagnosis of skin lesions [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, efficient segmentation of skin lesions is essential among these three stages because it helps to segregate skin lesions from the surrounding skins [ 7 , 13 ]. The success of the subsequent stages is heavily dependent on the preliminary segmentation output [ 4 , 5 , 6 , 10 , 14 , 15 , 16 , 17 , 18 ]. In addition, the segmentation process helps to identify local and global clinical features of the region of interest [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

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Despite the recent advances in immune therapies, melanoma remains one of the deadliest and most difficult skin cancers to treat. Literature reports that multifarious driver oncogenes with tumor suppressor genes are responsible for melanoma progression and its complexity can be demonstrated by alterations in expression with signaling cascades. However, a further improvement in the therapeutic outcomes of the disease is highly anticipated with the aid of humanoid assistive technologies that are nowadays touted as a superlative alternative for the clinical diagnosis of diseases. The development of the projected technology-assistive diagnostics will be based on the innovations of medical imaging, artificial intelligence, and humanoid robots. Segmentation of skin lesions in dermoscopic images is an important requisite component of such a breakthrough innovation for an accurate melanoma diagnosis. However, most of the existing segmentation methods tend to perform poorly on dermoscopic images with undesirable heterogeneous properties. Novel image segmentation methods are aimed to address these undesirable heterogeneous properties of skin lesions with the help of image preprocessing methods. Nevertheless, these methods come with the extra cost of computational complexity and their performances are highly dependent on the preprocessing methods used to alleviate the deteriorating effects of the inherent artifacts. The overarching objective of this study is to investigate the effects of image preprocessing on the performance of a saliency segmentation method for skin lesions. The resulting method from the collaboration of color histogram clustering with Otsu thresholding is applied to demonstrate that preprocessing can be abolished in the saliency segmentation of skin lesions in dermoscopic images with heterogeneous properties. The color histogram clustering is used to automatically determine the initial clusters that represent homogenous regions in an input image. Subsequently, a saliency map is computed by agglutinating color contrast, contrast ratio, spatial feature, and central prior to efficiently detect regions of skin lesions in dermoscopic images. The final stage of the segmentation process is accomplished by applying Otsu thresholding followed by morphological analysis to obliterate the undesirable artifacts that may be present at the saliency detection stage. Extensive experiments were conducted on the available benchmarking datasets to validate the performance of the segmentation method. Experimental results generally indicate that it is passable to segment skin lesions in dermoscopic images without preprocessing because the applied segmentation method is ferociously competitive with each of the numerous leading supervised and unsupervised segmentation methods investigated in this study.

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

Cited by 23 publications

References 30 publications

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

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