Breast cancer diagnosis in digitized mammograms using curvelet moments

Dhahbi, Sami; Barhoumi, Walid; Zagrouba, Ezzeddine

doi:10.1016/j.compbiomed.2015.06.012

Cited by 103 publications

(45 citation statements)

References 35 publications

(46 reference statements)

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“…Pavan et al, ( 2017) estimated breast density using post-processed digital mammograms and utilized an optimized FCM for classifying fibroglandular tissue in mammograms. Dhahbi et al, (2015) deals with feature extraction using the curvelet transforms and used k-nearest neighbor to classify the tumors as malignant or benign. Shi et al, (2018) estimated skin-air boundary using a gradient weight map and detected pectoral region unsupervised pixelwise labeling and used texture filter to detect calcifications.…”

Section: Breast Cancer Detection Using Crow Search Optimization Basedmentioning

confidence: 99%

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Parvathavarthini¹,

N²,

Shanthi³

2019

Asian Pac J Cancer Prev

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Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage.

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Section: Breast Cancer Detection Using Crow Search Optimization Basedmentioning

confidence: 99%

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Parvathavarthini¹,

N²,

Shanthi³

2019

Asian Pac J Cancer Prev

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“…Mammography is very effective and most commonly used technique for the early detection of breast cancer (Singh AK & Gupta B (2015), Pereira DC et al (2014), Dheeba J et al (2014), Dhahbi S et al (2015), Muramatsu C et al (2016), Rampun A et al (2017) ) it detects a very small change in the body even. The development of (CAD) computer assisted diagnostic systems for cancer such as breast cancer has become very important for hospital physicians and has become a top priority for many clinical researchers and centers (Pereira DC et al (2014)).…”

Section: Introductionmentioning

confidence: 99%

Benign and Malignant Breast Cancer Features Based on Region Characteristics

2019

ZJPAS

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Breast cancer is one of the most common malignant diseases among women. Mammography is at present one of the available method for early detection of abnormalities, which is related to breast cancer; there are different lesions that are breast cancer characteristic such as masses, which can be detected through this technique. In this work, we explore the application of density slicing and region growing techniques to the problem of finding the boundary of different breast tissue regions in mammograms and extracting these regions. The goal of the segmentation algorithm is to see if density slices images and region growing algorithm could separate different densities for the different breast patterns and calculating there features. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes and for extracting the cancer regions. Our proposed methodology for the segmentation of mammograms has been tested on Mini-MIAS database images. The final result is the segmentation of Region of I interest (ROI) with edge map and different features extraction for analysis where the extracted regions are saved as images and the texture features were calculated using Gray Level Co-Occurrence Matrix (GLCM) along 0^°such as contrast, correlation, homogeneity, sum variance, entropy, energy, mean values and others to differentiate between the Benign and Malignant images. It was found that the features such as contrast, correlation, smoothness, and difference variance are the optimal features to differentiate between benign and malignant regions where the other features are approximately the same. Also region growing method is very fast for extracting cancer regions. Our results are important to design artificial intelligence system to diagnostic breast cancer into benign and malignant cancer .

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“…In the literature, several approaches were proposed to segment microcalcifications [15][16][17][18] such as active contours [16,19], curvelet moments [20], wavelet analysis [21][22][23], fractal analysis [24][25][26], multifractal analysis [27,28] and morphological filters [29][30][31][32] in order to reduce human subjectivity in diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

Sahli¹

2018

IJMI

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This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: 'Sq-Sq' approach based on multifractal analysis and the 'MM' approach based on Mathematical Morphology. In fact, the results proved that the 'Sq-Sq' method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The 'Sq-Sq' approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.

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Breast cancer diagnosis in digitized mammograms using curvelet moments

Cited by 103 publications

References 35 publications

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Benign and Malignant Breast Cancer Features Based on Region Characteristics

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

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