A new conditional region growing approach for microcalcification delineation in mammograms

Touil, Asma; Kalti, Karim; Conze, Pierre-Henri; Solaiman, Basel; Mahjoub, Mohamed Ali

doi:10.1007/s11517-021-02379-x

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…Proposed a new segmentation method [ 26 ] based on the conditional region growing [ 12 , 13 ] approach for mammographic images, but the method is dependent on the order of the pixels processed thus results may vary for medical images. Introduce improved region growing (IRG) method [ 27 ] to segment lung tumor with less time and more accuracy. In the [ 28 ] proposed a method of segmenting medical images based on neural networks (NN) and fuzzy connectedness.…”

Section: Background and Related Workmentioning

confidence: 99%

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Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Gupta

Bhadauria

2022

Computational Intelligence and Neuroscience

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During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

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Section: Background and Related Workmentioning

confidence: 99%

“…Text Segmentation [1] Region Growing [12,13] Fuzzy connectedness [28] Graph Cut [27,28] Random Walk [28,29] Watershade [30,31] Hard Clustering [35][36][37][38] Soft Clustering [39][40][41][42] C-Means [45][46][47][48][49] K-Means [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] Fusion Based…”

Section: Machine Learningmentioning

confidence: 99%

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Gupta

Bhadauria

2022

Computational Intelligence and Neuroscience

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“…According to Touil et al [13] suggested a new conditional region growth (CRG) method for determining correct MC bounds beginning from a set of seed points. Regional maxima detection and superpixel analysis are used to find the beginning seed points.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast cancer segmentation using K-means clustering and optimized region-growing technique

Abdulla

Sagheer²,

Veisi

2022

Bulletin EEI

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Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.

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Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

Jha

Ahmad

Arya

et al. 2023

Journal of Healthcare Engineering

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The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

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A new conditional region growing approach for microcalcification delineation in mammograms

Cited by 3 publications

References 35 publications

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Breast cancer segmentation using K-means clustering and optimized region-growing technique

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

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