Fuzzy Based Image Segmentation

Basir, Otman; Zhu, Hui; Karray, Fakhri

doi:10.1007/978-3-540-36420-7_5

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Image Segmentation is probably one of the most important tasks in medical imaging. Here, the goal is to define regions that represent structural components of the human body [PXP00, BZK03]. Image segmentation is required in nearly every medical imaging pipeline as the definition of regions is required for most medical tasks, such as organ detection, computation of sizes, or detection of anomalies.…”

Section: Medical Imagingmentioning

confidence: 99%

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Gillmann

Saur

Wischgoll

et al. 2021

Computer Graphics Forum

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Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision‐making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state‐of‐the‐art in uncertainty‐aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty‐aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty‐aware medical imaging.

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Section: Medical Imagingmentioning

confidence: 99%

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Gillmann

Saur

Wischgoll

et al. 2021

Computer Graphics Forum

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show abstract

“…Classification is performed with the classic method of K-means and additionally more complicated methods of Fuzzy C-means (FCM) [6,7] and ANFIS technique that combines neural network and fuzzy inference engine methods.…”

mentioning

confidence: 99%

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

Dounis,

Avramopoulos,

Kallergi

2023

Applied Sciences

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Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.

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“…The method utilized in this paper is based on an initial user input [25]. With this input, the user contributes his or her knowledge on where the cutting tool and the background are located in the image into the segmentation process.…”

mentioning

confidence: 99%

An Industrial Vision System to Analyze the Wear of Cutting Tools

Gillmann

Post

Kirsch

et al. 2017

AMM

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The wear behavior of cutting tools directly affects the quality of the machined part. The measurement and evaluation of wear is a time consuming process and is subjective. Therefore, an image-based wear measurement that can be computed automatically based on given image series of cutting tools and an objective way to review the resulting wear is presented in this paper. The presented method follows the industrial vision system pipeline where images of cutting tools are used as input which are then transformed through suitable image processing methods to prepare them for the computation of a novel image based wear measurement. For multiple cutting tool settings a comparative visualization of the wear measurement outputs is presented. The effectiveness of the presented approach is shown by applying the method to measure the wear of four different cutting tool shapes.

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Fuzzy Based Image Segmentation

Cited by 8 publications

References 29 publications

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

An Industrial Vision System to Analyze the Wear of Cutting Tools

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