Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

2020 ◽  
Vol 9 (1) ◽  
pp. 16-32
Author(s):  
Kavya N ◽  
Sriraam N ◽  
Usha N ◽  
Bharathi Hiremath ◽  
Anusha Suresh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Texture Features ◽  
Automated Detection ◽  
Lesion Detection ◽  
Support Vector ◽  
Mean Variance ◽  
Aided Diagnosis ◽  
Cancer Lesion ◽  
Mammogram Images

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

CLASSIFICATION OF MAMMOGRAMS HAVING ARCHITECTURAL DISTORTION AND SPICULATED MASSES USING TEXTURE FEATURES

2021 ◽  
pp. 3-5
Author(s):  
D.B. Aghor ◽  
M.R. Banwaskar
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Screening Mammography ◽  
Support Vector ◽  
Architectural Distortion ◽  
Nonpalpable Breast Cancer ◽  
Current Systems ◽  
Aided Diagnosis ◽  
Mammogram Images

Architectural distortion is the third most common mammographic appearance of nonpalpable breast cancer, representing nearly 6% of abnormalities detected on screening mammography. Although its prevalence on mammography is small compared with calcication or visible mass, architectural distortion is also more difcult to diagnose because it can be subtle and variable in presentation. Early detection of breast cancer is possible by nding architectural distortion in monographic images. Spiculated masses account for about 14% of biopsied lesions and about 81% of these are malignant. Current CAD systems are dramatically better at detecting microcalcications than masses. The sensitivity is considerably lower for Spiculated Masses that are rated as "subtle" by radiologists Moreover, since current systems were devised with masses and calcications in mind, they don’t perform as well on other, less prevalent but still clinically signicant lesion types. In this paper, we propose a computer aided diagnosis system for distinguishing abnormal mammograms with architectural distortion or spiculated masses from normal mammograms. Five types of texture features GLCM, GLRLM, fractal texture, spectral texture and HOG features for the regions of suspicion are extracted. Support vector machine has been used as classier in this work. The proposed system yielded an overall accuracy of 97.29% for mammogram images collected from mini-MIAS database which is better as compared to existing methods.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

scholarly journals An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

2021 ◽  
Vol 9 ◽  
Author(s):  
Yifeng Dou ◽  
Wentao Meng
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Medical Institutions ◽  
High Level ◽  
Aided Diagnosis ◽  
Very High ◽  
Important Branch

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.

scholarly journals DETECÇÃO DE LESÕES EM MAMOGRAFIAS ATRAVÉS DA ASSIMETRIA DAS MAMAS E EXTRAÇÃO DE CARACTERÍSTICAS COM ÍNDICE DE GETIS-ORD

2013 ◽  
Vol 20 (3) ◽  
pp. 81
Author(s):  
Antônio Marcos Vieira Sales ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Cross Correlation ◽  
Lesion Detection ◽  
Cáncer De Mama ◽  
Support Vector ◽  
Processing Techniques

O câncer de mama é aquele que tem início nas células das mamas. A principal forma de prevençãoe diagnóstico precoce é através de exames de mamografia. Este trabalho tem como objetivo principalapresentar uma metodologia de auxílio à detecção de lesões em mamografias a partir da determinação de regiões suspeitas por nível de simetria. Técnicas de Processamento de Imagem foram usadas para preparar as mamografias e, em seguida, o nível de simetria entre a mama esquerda e a direita foi medido com coeficiente de correlação cruzada e distância euclidiana. O índice de Getis-Ord na sua forma geral foi usado para extrair características das imagens para treinar uma Máquina de Vetores de Suporte que classificouregiões das mamografias em lesão e não lesão. A metodologia, de modo geral, apresentou 80,11% de sensibilidade, 84,41% de especificidade e 84,38% de acurácia.Palavras-chave: Câncer de mama. Mamografia. Coeficiente de correlação cruzada. Distância euclidiana. Índice de Getis-Ord. Máquina de vetores de suporte. LESION DETECTION IN MAMMOGRAMS THROUGH THE ASYMMETRY OF THEBREASTS AND FEATURE EXTRACTION WITH INDEX GETIS-ORDAbstract: Breast cancer is one that starts in the cells of the breast. The main form of prevention and early diagnosis is through mammograms. This work has as main goal to present a methodology to aid in the detection of lesions on mammograms from the determination of suspicious regions by level of symmetry. Image processing techniques were used to prepare the mammograms and then the degree of symmetry between left and right breasts was measured using cross-correlation coefficient and Euclidean distance. The index Getis-Ord was used to extract features from images to train a Support Vector Machine which classified regions of mammograms in lesion and non-lesion. The methodology, in general, showed 80.11% sensitivity, 84.41% specificity and 84.38% accuracy.Keywords: Breast cancer. Mammography. Cross-correlation coefficient. Euclidean distance. Index Getis-Ord. Support vector machine. DETECCIÓN DE LESIONES EN LAS MAMOGRAFÍAS A TRAVÉS DE LA ASIMETRÍA DE LAS MAMAS Y EXTRACCIÓN DE CARACTERÍSTICAS CON EL ÍNDICE GETIS-ORDResumen: El cáncer de mama comienza en las células de los senos. La principal forma de prevención y diagnóstico precoz es a través de mamografías. Este trabajo tiene como objetivo principal presentar una metodología para ayudar en la detección de lesiones en las mamografías a partir de la determinación de las regiones sospechosas por nivel de simetría. Técnicas de procesamiento de imágenes se utilizaron para preparar las mamografías y luego el nivel de simetría entre el pecho izquierdo y derecho se midió utilizando el coeficiente de correlación cruzada y la distancia euclidiana. El índice Getis-Ord se utilizó para extraer características de las imágenes para formar una máquina de vectores de soporte que las regiones clasificadasde mamografías en lesión y no la lesión. La metodología, en general, mostró 80,11% de sensibilidad, especificidad 84,41% y 84,38% de precisión.Palabras clave: Cáncer de mama. Mamografía. Coeficiente de correlación cruzada. Distancia euclídea. Índice Getis-Ord. Máquina de vectores soporte.

scholarly journals CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS

2022 ◽  
Vol 23 (1) ◽  
pp. 187-199
Author(s):  
Suzani Mohamad Samuri ◽  
Try Viananda Nova ◽  
Bahbibi Rahmatullah ◽  
Shir Li Wang ◽  
Z.T Al-Qaysi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
The State ◽  
Classification Model ◽  
Support Vector ◽  
Digital Mammogram ◽  
Grey Level ◽  
Occurrence Matrix ◽  
Mammogram Images

Machine learning has been the topic of interest in research related to early detection of breast cancer based on mammogram images. In this study, we compare the performance results from three (3) types of machine learning techniques: 1) Naïve Bayes (NB), 2) Neural Network (NN) and 3) Support Vector Machine (SVM) with 2000 digital mammogram images to choose the best technique that could model the relationship between the features extracted and the state of the breast (‘Normal’ or ‘Cancer’). Grey Level Co-occurrence Matrix (GLCM) which represents the two dimensions of the level variation gray in the image is used in the feature extraction process. Six (6) attributes consist of contrast, variance, standard deviation, kurtosis, mean and smoothness were computed as feature extracted and used as the inputs for the classification process. The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques. 70% of the data were used as the training data and another 30% used as testing data. The result after ten (10) experiments show that, Support Vector Machine (SVM) gives the most consistent results in correctly classifying the state of the breast as ‘Normal’ or ‘Cancer’, with the accuracy of 99.4%, in training and 98.76% in testing. The SVM classification model has outperformed NN and NB model in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage. ABSTRAK: Pembelajaran mesin telah menjadi topik yang diminati dalam penyelidikan yang berkaitan dengan pengesanan awal kanser payudara berdasarkan imej mamogram. Dalam kajian ini, kami membandingkan hasil prestasi dari tiga (3) jenis teknik pembelajaran mesin: 1) Naïve Bayes (NB), 2) Neural Network (NN) dan 3) Support Vector Machine (SVM) dengan 2000 imej digital mammogram hingga teknik terbaik yang dapat memodelkan hubungan antara ciri yang diekstraksi dan keadaan payudara ('Normal' atau 'Cancer') dapat diperoleh. Grey Level Co-occurrence Matrix (GLCM) yang mewakili dua dimensi variasi tahap kelabu pada gambar digunakan dalam proses pengekstrakan ciri. Enam (6) atribut terdiri dari kontras, varians, sisihan piawai, kurtosis, min dan kehalusan dihitung sebagai fitur yang diekstrak dan digunakan sebagai input untuk proses klasifikasi. Eksperimen telah diulang selama sepuluh (10) kali untuk memeriksa kesesuaian prestasi semua teknik. 70% data digunakan sebagai data latihan dan 30% lagi digunakan sebagai data ujian. Hasil setelah sepuluh (10) eksperimen menunjukkan bahawa, Support Vector Machine (SVM) memberikan hasil yang paling konsisten dalam mengklasifikasikan keadaan payudara dengan betul sebagai 'Normal' atau 'Kanser', dengan akurasi 99.4%, dalam latihan dan 98.76% dalam ujian. Model klasifikasi SVM telah mengungguli model NN dan NB dalam kajian ini, dan ia menunjukkan bahawa SVM adalah pilihan yang baik untuk menentukan keadaan payudara pada peringkat awal.

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