An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms

Beheshti, Soosan; Ahmadi‐Noubari, H.; Fatemizadeh, Emad; Khalili, M.

doi:10.1007/s10278-013-9654-z

Cited by 22 publications

(11 citation statements)

References 11 publications

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“…DM helps to detect tumor before it develops further. Moreover, authors compare different methods that were used from 2011-2017 and conclude that SVM has the best result based on [4,5,6,7].…”

Section: Related Workmentioning

confidence: 99%

Using convolutional neural networks for breast cancer diagnosing

Naderan

Zaychenko

Napoli

2019

SRIT

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During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN's architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively.

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Section: Related Workmentioning

confidence: 99%

Using convolutional neural networks for breast cancer diagnosing

Naderan

Zaychenko

Napoli

2019

SRIT

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“…The concept of fractals was proposed by Mandelbrot to describe objects with irregular structures (13). For quantifying the complexity and self-similarity of the structure of an object, a measure known as the fractal dimension (FD) can be utilized (12).…”

Section: Fractal Analysismentioning

confidence: 99%

“…For classification of benign and spiculated masses, we used roughness in the boundary of masses as one of the main fractal features. Spiculated masses have rough variation in boundaries whereas the benign masses are round and with smooth variation (13). Therefore, the variation of FDs in different scales was utilized to extract important information for classification.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Rangayyan et al used the ruler method and the box-counting method for estimation of fractal dimension from contours of breast masses (12). Beheshti et al showed that extracting fractal features from contours of masses on different types of breast density mammograms was able to successfully classify breast tumors (13). The success of chaos analysis in biomedical research persuades us to use this method in the early detection of breast cancer as an important unsolved health problem.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction and Classification of Breast Tumors Using Chaos and Fractal Analysis on Dynamic Magnetic Resonance Imaging

Nirouei

Pouladian

Abdolmaleki

et al. 2016

Iran Red Crescent Med J

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Background: Breast cancer is one of the leading causes of death in the world. Early diagnosis of breast cancer can reduce the rate of mortality of this type of cancer. An increasing number of reports have confirmed the excellent sensitivity of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Despite the excellent sensitivity of DCE-MRI, there is still some difficulty in the prediction of malignancy in these patients because of the lack of the optimum guidelines for the interpretation of breast magnetic resonance (MR) studies as well as the reported overlap in T1 and T2 relaxation times. Objectives: The aim of this study was to extract significant features from MRI images of the breast using chaos, fractal and time series analysis and to classify breast tumors into malignant and benign using the calculated features. Methods: In this research, we utilized the chaos theory and fractal analysis in the interpretation of breast tumors on DCE-MRI. This cross-sectional study was done at Pardisnoor imaging center during years 2015 and 2016 in Iran. Our sample size was 18 mass lesions, which were randomly selected among patients with BIRAD 3 and BIRAD 4 classification by the expert radiologist. The analysis was performed after injecting patients with a contrast agent and 18 mass lesions were extracted from dynamic MR images. After preprocessing and segmentation stages, time series of the tumor was generated for each MR image. The largest Lyapunov exponent (LLE) and statistical parameters for each mass lesion were extracted. Also, fractal analysis was utilized to extract meaningful features from mass contour to evaluate the roughness of tumor margin. Results: We found that the value of LLE in malignant tumors was higher than benign mass lesions. The obtained results demonstrated that chaos and time series features, such as LLE and non-circularity of the tumor, were the best parameters among all features. Conclusions: The extracted descriptors can improve the performance of classifiers in the early detection of breast cancer. Significant shape features can also help radiologists increase diagnosis accuracy in classification of suspicious breast masses.

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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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An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms

Cited by 22 publications

References 11 publications

Using convolutional neural networks for breast cancer diagnosing

Using convolutional neural networks for breast cancer diagnosing

Feature Extraction and Classification of Breast Tumors Using Chaos and Fractal Analysis on Dynamic Magnetic Resonance Imaging

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

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