On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based—namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)—and distance-based metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% ± 6.50 (sFCM), JI = 65.90% ± 8.14 (sFCM), sensitivity = 77.84% ± 8.72 (FCM), specificity = 87.10% ± 8.24 (sFCM), FPR = 0.14 ± 0.12 (sFCM), and FNR = 0.22 ± 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 ± 0.90 (sFCM), MaxD = 4.04 ± 2.87 (sFCM), and HD = 2.21 ± 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.

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Texture-Based Watershed 3D Medical Image Segmentation Based on Fuzzy Region Growing Approach

Advances in Intelligent Systems and Computing - Advances in Computational Intelligence ◽

10.1007/978-981-10-2525-9_23 ◽

2016 ◽

pp. 233-243

Author(s):

Rajaram M. Gowda ◽

G. M. Lingaraju

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Region Growing ◽

Medical Image Segmentation ◽

Fuzzy Region

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Research and Improvement of Live-Wire Interactive Algorithm for Medical Image Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1065 ◽

2012 ◽

Vol 182-183 ◽

pp. 1065-1068 ◽

Cited By ~ 2

Author(s):

Ji Ying Li ◽

Jian Wu Dang

Keyword(s):

Image Segmentation ◽

Anisotropic Diffusion ◽

Medical Image ◽

Optimal Path ◽

Region Growing ◽

Medical Image Segmentation ◽

Interactive Segmentation ◽

Diffusion Filtering ◽

Interactive Algorithm ◽

Region Growing Segmentation

Traditional Live Wire algorithm distinguished the strength edge of objectives uneasily and executive speed of algorithm is slow. For these problems, an improved Live-Wire algorithm is proposed. First it implements anisotropic diffusion filtering to images and constructs a new expense function, then combined with region growing segmentation algorithm, it implements interactive segmentation to medical images. Improved algorithm avoids the shortcomings of the traditional Live-wire algorithm which is sensitive to noise and can not effectively distinguish the edge of the strength, also reduces the time and blindness of dynamic programming to find the optimal path and improves the accuracy and implementation efficiency of the image segmentation.

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Researcher of Region Growing Medical Image Segmentation Based on Adaboost Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.142.21 ◽

2010 ◽

Vol 142 ◽

pp. 21-25

Author(s):

Peng Wang ◽

X.F. Ye ◽

Shi Wei Yin ◽

Shao Chen Kang ◽

Jing Lei Xin

Keyword(s):

Image Segmentation ◽

Background Subtraction ◽

Medical Image ◽

Region Growing ◽

Medical Image Segmentation ◽

Segmentation Algorithm ◽

Adaboost Algorithm ◽

Actual Application ◽

Region Extraction ◽

Seed Pixel

To obtain better region extraction results of medical image, a new segmentation algorithm is proposed based on improved Adaboost algorithm. The seed pixel is selected with background subtraction. The neighborhood point is judged. The primary selected seed is calibrated with label, and then the range of seed is reduced through growing label and the maximal saliency. The optimized Adaboost algorithm is taken as growing criterion to optimally combine the scrappy region when the region growing is over. The experiment result shows that the accuracy and robustness of the algorithm both meet the actual application required.

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Region Growing for Medical Image Segmentation Using a Modified Multiple-seed Approach on a Multi-core CPU Computer

IFMBE Proceedings - The 15th International Conference on Biomedical Engineering ◽

10.1007/978-3-319-02913-9_29 ◽

2014 ◽

pp. 112-115 ◽

Cited By ~ 5

Author(s):

Agus Pratondo ◽

Sim Heng Ong ◽

Chee Kong Chui

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Region Growing ◽

Medical Image Segmentation

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Computed Tomography Image Segmentation Using Edge Correction Algorithm for Refractory Mycoplasma Pneumonia in Children

Scientific Programming ◽

10.1155/2021/3578971 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Lijuan Wu ◽

Jianwei Ji ◽

Shiyong Zhao ◽

Jiaolei Chen

Keyword(s):

Computed Tomography ◽

Image Segmentation ◽

Recovery Rate ◽

Medical Image ◽

Clinical Characteristics ◽

False Negative ◽

Fiberoptic Bronchoscopy ◽

Medical Image Segmentation ◽

Correction Algorithm ◽

Imaging Characteristics

Objective. It is to study the application of edge correction algorithm (ECA) in computed tomography (CT) medical image segmentation, explore its guiding significance in the analysis of clinical characteristics of children with refractory mycoplasma pneumoniae (RMPP), and discuss the therapeutic value of fiberoptic bronchoscopy bronchoalveolar lavage (BAL) for RMPP. Methods. The accuracy of ECA in CT medical image segmentation of children with RMPP was compared with that of the watershed segmentation algorithm (WSA) and swarm intelligence optimization algorithm (SIOA). The clinical characteristics and the imaging characteristics of 80 children with RMPP admitted to hospital from January 2018 to January 2020 were retrospectively analyzed based on the ECA. All children were divided into a lavage group (BAL group, n = 69) and a nonlavage group (non-BAL group, n = 11) according to whether fiberoptic bronchoscopy and BAL were performed. Bronchoscopy was adopted to analyze the cytological characteristics of BAL fluid (BALF) in children, and the recovery rate and the total effective rate of the two groups of children were observed and compared. Results. The overall accuracies (OAs) of the three ECAs (Roberts operator (RO), Sobel operator (SO), and Prewitt operator (PO)) were higher than that of WSA and SIOA, their false negative rate (FNR) and false positive rate (FPR) were small, and their denoising performance was superior to that of WSA and SIOA. The main clinical manifestations of all children were high fever, irritating dry cough, and few early signs. The results of chest CT examination were mainly manifested as patchy or large-scale consolidation, two lung mesh or small nodular shadows, and atelectasis. 69 cases with fiberoptic bronchoscopy showed swelling and congestion of the bronchial mucosa at the lesion site with visible viscous secretions, which was consistent with the imaging changes. The total number of cells in the BALF of children increased ( P < 0.05 ), which mainly represented the increase of neutrophils ( P < 0.05 ). The recovery rate of children with lavage (81.16%) was higher dramatically than that of the nonlavage group (45.45%). Conclusion. The ECA had good accuracy and denoising performance in lung CT image segmentation. The clinical characteristics, imaging characteristics, and cytological components of children had changed when they suffered from the RMPP, and fiberoptic bronchoscopy lavage had a therapeutic effect on it.

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