Bilateral analysis based false positive reduction for computerâ aided mass detection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134848/1/mp6612.pd

Improvement of mammographic mass characterization using spiculation measures and morphological features

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135119/1/mp1548.pd

deep learning based segmentation of breast masses in dedicated breast ct imaging radiomic feature stability between radiologists and artificial intelligence

Abstract A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks' lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation

A similarity study of contentâ based image retrieval system for breast cancer using decision tree

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134877/1/mp0277.pd

A novel shape feature to classify microcalcifications

Clinical evident shows that the shape of mammographic calcification is an indicator of the pathology. Microcalcifications (MC) with rough shape are early signs of malignant breast cancer. This thesis proposed a shape metric to help radiologist in classifying regions of interest. Region growing and gradient vector flow algorithm are used to obtain the contour of MC to calculate the normalized distance signature. A three level wavelet decomposition with a Daubechies eight tap wavelet is used to provide a bandpass function and extract the desired shape feature of the MC. A comparison with previously used shape features such as compactness, moment, Fourier descriptors is provided. 58 malignant and 125 benign cases, totaling 368 individual MC, are tested by the proposed method and previously used shape features

KLASIFIKASI SEL SERVIKS PADA CITRA PAP SMEAR BERDASARKAN FITUR BENTUK DESKRIPTOR REGIONAL DAN FITUR TEKSTUR UNIFORM ROTATED LOCAL BINARY PATTERN

Perubahan orientasi objek pada saat akuisisi memerlukan metode ekstraksi fitur yang invariant terhadap rotasi. Ekstraksi fitur tekstur yang telah digunakan dalam kombinasi fitur sebelumnya untuk klasifikasi sel serviks pada dataset Herlev antara lain homogenitas GLCM dan Local Binary Pattern Histogram Fourier (LBP-HF). Namun perhitungan GLCM sensitif terhadap rotasi dan transformasi fourier LBP-HF mengabaikan penataan struktur histogram dengan hanya mempertimbangkan magnitude spektrum transformasi sehingga kehilangan beberapa informasi diskriminatif dan informasi frekuensi citra.Penelitian ini mengusulkan kombinasi fitur bentuk deskriptor regional dan fitur tekstur Uniform Rotated Local Binary Pattern (uRLBP). uRLBP merupakan metode ekstraksi fitur yang dapat mengatasi kelemahan metode tekstur sebelumnya dengan mengatur arah referensi lokal yang dapat mempertahankan informasi orientasi lokal dan informasi diskriminatif citra sehingga mencapai invariant terhadap rotasi. Pengujian dilakukan dengan membandingkan hasil klasifikasi metode yang diusulkan dengan metode pada penelitian sebelumnya dalam melakukan klasifikasi sel serviks pada citra pap smear.Hasil pengujian menunjukkan bahwa metode yang diusulkan mampu mengklasifikasikan sel serviks lebih optimal dibandingkan metode kombinasi fitur bentuk & fitur tekstur homogenitas GLCM dan metode kombinasi fitur bentuk & fitur tekstur LBP-HF. Nilai akurasi menggunakan metode klasifikasi Fuzzy k-NN adalah 91.59% untuk dua kategori sel dan 67.89% untuk tujuh kelas sel

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists’ performance in finding and discriminat- ing between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detec- tion on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Fea- ture extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification tech- niques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed

Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network

We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48961/2/m70308.pd