Automated saliency-based lesion segmentation in dermoscopic images

Ahn, Euijoon; Bi, Lei; Jung, Youn Hyun; Li, Changyang; Fulham, Michael; Feng, David Dagan

doi:10.1109/embc.2015.7319025

Cited by 50 publications

(34 citation statements)

References 16 publications

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“…The algorithmic implementation of the method of perceptual color difference saliency (PCDS) is succinctly outlined based on mathematical equations (1)- (17). The asymptotic time complexity of the PCDS algorithm is ( × × 3) for an input color image of dimensions × × 3.…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…However, the application of saliency methods for segmentation of skin lesion is relatively new [13,17,37]. Saliency segmentation computes the most informative region in an image based on human vision perception such that salient and nonsalient parts become foreground region (skin lesion) and background region (healthy skin), respectively.…”

Section: Saliency Based Segmentationmentioning

confidence: 99%

“…The method of saliency based segmentation has emerged as an important tool for medical image analysis because of its capability to identify salient objects in images [17,18]. Its application in computer vision is largely inspired by the findings that human vision perception has a higher probability to focus on the part of an image that carries useful information [17,19].…”

Section: Introductionmentioning

confidence: 99%

“…Its application in computer vision is largely inspired by the findings that human vision perception has a higher probability to focus on the part of an image that carries useful information [17,19]. The cognitive properties of visual saliency incorporated into the conventional saliency segmentation methods are based upon local or global visual rarity such as contrast prior, color prior, brightness prior, and center prior.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Algorithm Implementationmentioning

confidence: 99%

Section: Saliency Based Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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“…In PH2 dataset, for performance evaluation, we compared with five state-of-the-art methods: four methods were hand-craft based models; one was a deep learning based model. In hand-craft based models, AT [8], Level Set Active Contours [9], Abedini's Method [3] and SSLS [10] were used. In a deep learning based model, U-net [4] was used.…”

Section: B Performance Evaluation Of Medical Image Segmentationmentioning

confidence: 99%

Iterative deep convolutional encoder-decoder network for medical image segmentation

Kim

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

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Dynamic weight HiLo attention network for medical image multiple organ segmentation

Zhao,

Li,

Liu

2023

Int J Imaging Syst Tech

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In recent years, there has been a surge of research in the field of medical image segmentation using hybrid CNN‐Transformer network architectures. Most of these studies leverage the attention mechanism of ViT to overcome the limitations of CNN architectures in capturing long‐range dependencies. However, these hybrid model approaches also have some potential drawbacks. First, due to the heavy reliance of the Transformer's attention mechanism on global information, it can lead to a significant increase in computational cost when dealing with high‐resolution input images. Furthermore, the convolutional and attention mechanisms in hybrid models have different interpretability in information extraction and decision‐making, which poses a challenge for the interpretability of the convolutional part. Our proposed model, DWHA, addresses these limitations and outperforms state‐of‐the‐art models in a range of medical image segmentation tasks, such as abdominal multiorgan segmentation, automatic cardiac diagnosis, neurostructure segmentation, and skin lesion segmentation, achieving significantly superior performance. Specifically, on the abdominal multiorgan segmentation dataset, DWHA outperformed the previous state‐of‐the‐art baseline by 0.57%; on the neurostructure segmentation dataset, it achieved an improvement of 1.17%; and on the skin lesion segmentation dataset, it achieved an improvement of 0.91%. These significant improvements suggest that DWHA may become the preferred model in the field of medical image segmentation.

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Automated saliency-based lesion segmentation in dermoscopic images

Cited by 50 publications

References 16 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Iterative deep convolutional encoder-decoder network for medical image segmentation

Dynamic weight HiLo attention network for medical image multiple organ segmentation

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