Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.pd

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/1/mp7345_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/2/mp7345.pd

Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/1/mp4498.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/2/mp4498_am.pd

Computerâ aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134920/1/mp0955.pd

Efficient suspicious region segmentation algorithm for computer aided diagnosis of breast cancer based on tomosynthesis imaging

Computer aided diagnostic tool can aid the radiologist in the early detection of breast cancer. Even though mammography is considered to be the gold standard for breast cancer detection, it is limited by the spatial superposition of tissue. This limitation is the result of a using a two dimensional, (2D), representation of a three dimensional, (3D), structure. The limitation contributes to and results in misclassification of breast cancers. Tomosynthesis is a limited-angle 3D imaging device that overcomes this limitation by representing the breast structure with 3D volumetric data.This research, on tomosynthesis imaging, was a critical module of a larger research endeavor for the detection of breast cancer. Tomosynthesis is an emerging state-of-the-art 3D imaging technology. The purpose of this research was to develop a tomosynthesis based, efficient suspicious region segmentation, procedure for the breast to enhance the detection and diagnosis of breast cancer. The 3D breast volume is constructed to visualize the 3D structure of the breast region. Advanced image processing and analysis algorithms were developed to remove out-of-plane artifacts and increase the Signal Difference to Noise Ratio, (SDNR), of tomosynthetic images. Suspicious regions are extracted from the breast volume using efficient and robust clustering algorithms.A partial differential equation based non-linear diffusion method was modified to include the anisotropic nature of tomosynthesis data in order to filter out the out-of-plane artifacts, which are termed tomosynthetic noise , and to smooth the in-plane noise. Fuzzy clustering algorithms were modified to include spatial domain information to segment suspicious regions. A significant improvement was observed, both qualitatively and quantitatively, in segmentation of the filtered data over the non-filtered data. The 3D segmentation system is robust enough to be used for statistical analysis of huge databases

Psychophysical similarity based feature selection for nodule retrieval in CT

The emerging paradigms in cancer research indicate the need for a multi-perspective and multimodal screening approach for early lung cancer diagnosis to increase the probability of clinical resection. Currently, no standalone screening methodology has been proven to suffice any clinical diagnosis. Computed tomography(CT) has been proved to present abnormality at an early stage with less impact on survival rate in population studies. Nevertheless, because of its non-invasive characteristic, it can be used for diagnosis, prognosis and visualization of tumor. Studies have shown that Computer aided diagnosis (CAD) as a second reader can perform in a similar capacity as a radiologist. The sensitivity and specificity can further be improved if CAD based CT is combined with content-based image retrieval (CBIR), where display of similar diagnostic proven cases can speed up the radiological analysis and also increase the effectiveness of the radiologist. Both the classification and the retrieval tasks have much to do with the human visual system (HVS). Objectiveness does not exist in the ability to detect and diagnose cancerous tissue on the CT by the HVS, nevertheless the CAD which is based on a computer vision system (CVS), can only perform as well as the HVS. The proposed approach for classification and retrieval relies on the mapping between the HVS and a CVS. The segmentation of lung nodule is a prerequisite for both the CAD and CBIR tasks. A novel segmentation method is proposed which exploits the time map relationship between the hessian and level set based segmentations. The mapping is generated using the statistics from the hessian segmentation through a weighted regression model trained a priori. It is shown that the proposed computer based segmentation can perform as efficiently as the visual description of the radiologist to aid the retrieval type of tasks. The method exploits the intensity invariant properties of the eigenvalues from the hessian decomposition and the time crossing map from the level set approach to accurately determine the nodule boundary. The classification part demonstrates that, for optimum selection of features, each feature should be analyzed individually and collectively with other features to evaluate the impact on the CAD system based on the class representation. This methodology will ultimately aid in improving the generalization capability of the classification module for early lung cancer diagnosis. Nonparametric correlation coefficients, multiple regression analysis and principle component analysis (PCA) were used to map the relationship between the represented features from the 4 radiologists and the computed features. Artificial neural network (ANN) is used for classification of benign and malignant nodules to test the hypotheses obtained from the mapping analysis. The final part of the dissertation work includes a lung nodule based similar volume retrieval approach based on the signature generated from the selection in the high-level feature space. The signature is generated by representing the psychophysical similarity between low-level (content) and high-level (semantic) features as a Max-flow/Min-cut graph cut solution. The quantification of the similarity is done using a non-parametric rank correlation coefficient. The retrieval works on a hierarchical framework to emulate the clinical diagnosis processes. The selection and weightage of content features is automatically generated thus providing the necessary abstraction to the radiologist. The retrieval accuracy of the proposed approach is done in content domain for the five models generated in the semantic domain

Computer- aided diagnosis in the era of deep learning

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155515/1/mp13764.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155515/2/mp13764_am.pd

Similarity Based False-Positive Reduction for Breast Cancer using Radiographic and Pathologic Imaging Features

ABSTRACT Mammography reading by radiologists and breast tissue image interpretation by pathologists often leads to high False Positive (FP) Rates. Similarly, current Computer Aided Diagnosis (CADx) methods tend to concentrate more on sensitivity, thus increasing the FP rates. A novel method is introduced here which employs similarity based method to decrease the FP rate in the diagnosis of microcalcifications. This method employs the Principal Component Analysis (PCA) and the similarity metrics in order to achieve the proposed goal. The training and testing set is divided into generalized (Normal and Abnormal) and more specific (Abnormal, Normal, Benign) classes. The performance of this method as a standalone classification system is evaluated in both the cases (general and specific). In another approach the probability of each case belonging to a particular class is calculated. If the probabilities are too close to classify, the augmented CADx system can be instructed to have a detailed analysis of such cases. In case of normal cases with high probability, no further processing is necessary, thus reducing the computation time. Hence, this novel method can be employed in cascade with CADx to reduce the FP rate and also avoid unnecessary computational time. Using this methodology, a false positive rate of 8% and 11% is achieved for mammography and cellular images respectively

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168449/1/mp14678_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168449/2/mp14678.pd