Automated assessment of breast tissue density in digital mammograms

Subashini, T. S.; Ramalingam, V.; Palanivel, S.

doi:10.1016/j.cviu.2009.09.009

Cited by 96 publications

(49 citation statements)

References 20 publications

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“…In recent years, many kinds of features have been reported for breast mass classification like texture based features, region based features, image structure features, position related features, and shape-based features which are described and utilized in the CAD systems [31]. In our study a set of six novel features based on texture, region, and shape characteristics were extracted from the original images which are not applied in breast mass classification before and are defined as follows:…”

Section: Segmentation and Mass Extractionmentioning

confidence: 99%

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

2010

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The purpose of this research was evaluating novel shape and texture feature' efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven benign cases and thirty-three malignant cases) to evaluate our method. The classification results showed an overall accuracy of 95.00%, sensitivity of 90.91%, specificity of 97.87%, positive predictive value of 96.77%, negative predictive value of 93.88%, and Matthew's correlation coefficient of 89.71%. The experimental results declare that our proposed method is actually a beneficial tool for the diagnosis of the breast cancer and can provide a second opinion for a physician's decision or can be used for the medicine training especially when coupled with other modalities.

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Section: Segmentation and Mass Extractionmentioning

confidence: 99%

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

2010

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“…Subashini et al presented a method [17] which uses 9 statistical features and the support vector machine classifier obtaining correct classification of 95.44 % on the MIAS database mammograms. Such high classification accuracy can be explained by the fact that they used only 43 out of 322 available images from the MIAS database and the result of correct classification would probably decrease if the entire database was considered.…”

Section: Overview Of the Previous Workmentioning

confidence: 99%

Breast Density Classification Using Multiple Feature Selection

2012

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Original scientific paperMammography as an x-ray method usually gives good results for lower density breasts while higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms knowledge about breast density can be useful for setting an appropriate decision threshold in order to produce more accurate detection. Because the overall intensity of mammograms is not directly correlated with the breast density we have decided to observe breast density as a texture classification problem. In this paper we propose breast density classification using feature selection process for different classifiers based on grayscale features of first and second order. In feature selection process different selection methods were used and obtained results show the improvement on overall classification by choosing the appropriate method and classifier. The classification accuracy has been tested on the mini-MIAS database and KBD-FER digital mammography database with different number of categories for each database. Obtained accuracy stretches between 97.2 % and 76.4 % for different number of categories.Key words: Breast Density, Feature Selection, Haralick Features, Soh Features, Classification Klasifikacija dojki prema gustoći izborom značajki. Mamografija je rendgenska metoda koja daje dobre rezultate pri slikanju dojki koje imaju manju gustoću, dok joj osjetljivost značajno opada pri snimanju dojki veće gustoće i time može doći do lažno pozitivnih rezultata. Poznavanje gustoće dojke može biti korisno kod algoritama za automatsku detekciju zbog mogućnosti odredivanja praga odluke na osnovi tog znanja. S obzirom na to da ukupni intenzitet pojedinog mamograma nije izravno povezan s gustoćom, odlučili smo se promatrati gustoću kao problem klasifikacije teksture. U ovom radu predlažemo klasifikaciju dojki prema gustoći izborom izdvojenih značajki intenziteta prvog i drugog reda za različite klasifikatore. Za odredivanje prikladnih značajki koristili smo različite metode i tako dobivene značajke pokazale su bolju točnost klasifikacije za odabrane klasifikatore. Točnost klasifikacije testirali smo na bazi mamografskih slika mini-MIAS i bazi digitalnih mamografskih slika KBD-FER s različitim brojem kategorija u koje su slike bile podijeljene. Postignuta točnost klasifikacije proteže se izmedu 97,2 % i 76,4 % za različit broj kategorija u koje su mamogrami podijeljeni.

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“…The basic function of the perceptron is shown in Fig. 4 is analogous to the synaptic activities of a biological neuron [16]. In a layered network structure, the neural element may receive its input from an input vector or other neural elements.…”

Section: Classificationmentioning

confidence: 99%

A Content Based Approach to Medical X-Ray Image Retrieval using Texture Features

Ganesan

Subashini

2014

IJCT

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Of late, the amount of digital X-ray images that are produced in hospitals is increasing incredibly fast. Efficient storing, processing and retrieving of X-ray images have thus become an important research topic. With the exponential need that arises in the search for the clinically relevant and visually similar medical images over a vast database, the arena of digital imaging techniques is forced to provide a potential and path-breaking methodology in the midst of technical advancements so as to give the best match in accordance to the user's query image. CBIR helps doctors to compare X-rays of their current patients with images from similar cases and they could also use these images as queries to find the similar entries in the X-ray database. This paper focuses on six different classes of X-ray images, viz. chest, skull, foot, spine, pelvic and palm for efficient image retrieval. Initially the various X-rays are automatically classified into the six-different classes using BPNN and SVM as classifiers and GLCM co-efficient as features for classification. Indexing is done to make the retrieval fast and retrieval of similar images is based on the city block distance.Key Words: Content based image retrieval; back propagation neural network; support vector machine; gray level cooccurrence matrix and city block distance method.

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Automated assessment of breast tissue density in digital mammograms

Cited by 96 publications

References 20 publications

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

Breast Density Classification Using Multiple Feature Selection

A Content Based Approach to Medical X-Ray Image Retrieval using Texture Features

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