Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms

Radović, Miloš; Milosevic, Marina; Ninkovic, Srdjan; Filipović, Nenad; Peulić, Aleksandar

doi:10.3233/thc-151034

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…In general, the radiologist can recognize most of tumors in the mammograms especially in the case of fatty tissues. Unfortunately, other types with high density are elusive and hard to be detected [11][12][13]. The difference between fatty and dense tissues mammograms can be easily viewed as shown in Figure 1.…”

Section: Methodology Of the Proposed Schemementioning

confidence: 99%

Detection of partially overlapped masses in mammograms

Alhabib

2020

IJEECS

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<span>Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis.</span>

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Section: Methodology Of the Proposed Schemementioning

confidence: 99%

Detection of partially overlapped masses in mammograms

Alhabib

2020

IJEECS

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“…18 features were extracted from GLCM. These features are as follows: autocorrelation, correlation, contrast, cluster shade, cluster prominence, dissimilarity, entropy, sum entropy, difference entropy, energy, sum average, maximum probability, homogeneity, sum of squares variance, sum variance, information measure of correlation1, inverse difference moment and information measure of cor-relation2 [19,20]. GLCM provides more number of features than histogram.…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Application of texture analysis method for mammogram density classification

Nithya

Santhi

2017

J. Inst.

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A: Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification. K: Medical-image reconstruction methods and algorithms, computer-aided diagnosis; X-ray mammography and scinto-and MRI-mammography 1Corresponding author.

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“…A specific aspect of these computations is that it is important to maintain full processing traces as an integral part of the results, since the latter is at the end of complex and deep computational pipelines whose outcome depends strongly on chosen parameters and configurations. This is particularly true now for the analysis of data coming from NGS and it will become equally relevant with the expected diffusion of Computer Aided Diagnosis systems [9]. Analogously, the explosive diffusion of digital data acquisition devices for biomedical applications, ranging from fully traced clinical procedures [10] to IOT personal health acquisition devices [11] has dramatically increased the amount of context information that can be attached to phenotypic information.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

Delussu¹,

Lianas²,

Frexia³

et al. 2016

Preprint

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This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.

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Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms

Abstract: By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.

Cited by 10 publications

References 20 publications

Detection of partially overlapped masses in mammograms

Detection of partially overlapped masses in mammograms

Application of texture analysis method for mammogram density classification

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

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