Hybrid Mammogram Segmentation Using Watershed and Region Growing

Rmili, Mohammed; Moutaouakkil, Abdelmajid El; Saleck, Mousatapha M.

doi:10.1007/978-3-030-91738-8_3

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In the literature, this dataset has been used for texture and deep learning-based feature extraction in the experimental setup (Sharma and Purwar, 2020;Sharma and Purwar, 2022) good classification performance on it. Recently, a series of studies used a mini-MIAS dataset for performance evaluation on various deep learning models (Patel and Hadia, 2023;Ranjbarzadeh et al, 2022;Kumar et al, 2022;Rmili et al, 2022;Kulkarni and Rabidas, 2023). Furthermore, Sharma and Purwar (2023) augmented the ROI images extracted from this dataset and achieved an average enhancement of more than 8% of classification accuracy when evaluated over 24 classifiers.…”

Section: Stages Of Cad Modelsmentioning

confidence: 99%

Computer-Aided Detection and Diagnosis of Breast Cancer: a Review

Sharma,

Purwar

2024

ADCAIJ

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Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches.

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Section: Stages Of Cad Modelsmentioning

confidence: 99%

Computer-Aided Detection and Diagnosis of Breast Cancer: a Review

Sharma,

Purwar

2024

ADCAIJ

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“…The results showed that the second algorithm of clustering is better than the first algorithm in terms of accuracy. In [11] Adaptive Limited to enhance the breast lesions by reducing the image noise. Then, a Multilevel Otsu Thresholding is applied to detect breast lesions using the e region of interest tool at different intensity levels.…”

Section: Related Workmentioning

confidence: 99%

Otsu’s Thresholding for Semi-Automatic Segmentation of Breast Lesions in Digital Mammograms

Saleck¹,

Dyla²,

Mahmoud³

et al. 2022

IJACSA

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In Maghreb countries, breast cancer considered one of the leading causes of mortality between females. A screening mammogram is a method of taking low energy level xray images of the human breast to identify the early symptoms of breast cancer. The shape and contour of the lesion in digitized mammograms are two effective features that allow the radiologists to distinguish between benign and malignant tumors. We propose in this paper a new approach based on Otsu's thresholding method for semi-automatic extraction of lesion boundaries from mammogram images. This approach attempts to find the best threshold value where the weighted variance between the lesion and normal tissue pixels is the least. In the first step, the median filter is used for removing noise within the region of interest (ROI). In the second step, a global threshold decrement was started in order to get the proper range of pixels in which the breast lesion could be segmented by Otsu's thresholding method with high accuracy. The technique of mathematical morphology, especially opening operation is used in this work for removing small objects from the ROI while preserving the shape and size of larger objects that represent the tumors. We evaluated our proposal using 21 images obtained from Mini-MIAS database. Experimental results show that our proposal achieves better results than many previously explored methods.

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Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images

Kubíček

Varysova

Cerny

et al. 2022

Sensors

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The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur’s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation’s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.

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Hybrid Mammogram Segmentation Using Watershed and Region Growing

Cited by 3 publications

References 16 publications

Computer-Aided Detection and Diagnosis of Breast Cancer: a Review

Computer-Aided Detection and Diagnosis of Breast Cancer: a Review

Otsu’s Thresholding for Semi-Automatic Segmentation of Breast Lesions in Digital Mammograms

Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images

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