Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Susana Lai-Yuen

Keywords

Medical Imaging, Non-linear Regression, Organ Location, Prediction,, SVM

Abstract

Pelvic organ prolapse (POP) is a major health problem that affects women. POP is a herniation of the female pelvic floor organs (bladder, uterus, small bowel, and rectum) into the vagina. This condition can cause significant problems such as urinary and fecal incontinence, bothersome vaginal bulge, incomplete bowel and bladder emptying, and pain/discomfort. POP is normally diagnosed through clinical examination since there are few associated symptoms. However, clinical examination has been found to be inadequate and in disagreement with surgical findings. This makes POP a common but poorly understood condition. Dynamic magnetic resonance imaging (MRI) of the pelvic floor has become an increasingly popular tool to assess POP cases that may not be evident on clinical examination. Anatomical landmarks are manually identified on MRI along the midsagittal plane to determine reference lines and measurements for grading POP. However, the manual identification of these points, lines and measurements on MRI is a time-consuming and subjective procedure. This has restricted the correlation analysis of MRI measurements with clinical outcomes to improve the diagnosis of POP and predict the risk of development of this disorder.

The main goal of this research is to improve the diagnosis of pelvic organ prolapse through a model that automatically extracts image-based features from patient specific MRI and fuses them with clinical outcomes. To extract image-based features, anatomical landmarks need to be identified on MRI through the localization and segmentation of pelvic bone structures. This is the main challenge of current algorithms, which tend to fail during bone localization and segmentation on MRI. The proposed research consists of three major objectives: (1) to automatically identify pelvic floor structures on MRI using a multivariate linear regression model with global information, (2) to identify image-based features using a hybrid technique based on texture-based block classification and K-means clustering analysis to improve the segmentation of bone structures on images with low contrast and image in homogeneity, (3) to design, test and validate a prediction model using support vector machines with correlation analysis based feature selection to improve disease diagnosis.

The proposed model will enable faster and more consistent automated extraction of features from images with low contrast and high inhomogeneity. This is expected to allow studies on large databases to improve the correlation analysis between MRI features and clinical outcomes. The proposed research focuses on the pelvic region but the techniques are applicable to other anatomical regions that require automated localization and segmentation of multiple structures from images with high inhomogeneity, low contrast, and noise. This research can also be applicable to the automated extraction and analysis of image-based features for the diagnosis of other diseases where clinical examination is not adequate. The proposed model will set the foundation towards a computer-aided decision support system that will enable the fusion of image, clinical, and patient data to improve the diagnosis of POP through personalized assessment. Automating the process of pelvic floor measurements on radiologic studies will allow the use of imaging to predict the development of POP in predisposed patients, and possibly lead to preventive strategies.

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