Pandey, Dinesh (2019) Multidimensional medical image analysis with automatic segmentation techniques. PhD thesis, Victoria University.

Abstract

The advancement of medical imaging techniques such as fundus photography and breast magnetic resonance imaging (MRI) has shown tremendous improvement in the quality of multidimensional image produced. The image segmentation technology is used to partition the medical image into different regions for accurate identification and segregation of diseased area. Hence, the medical image is a vital entity to diagnose several pathological conditions. However, Multidimensional medical image analysis with automatic segmentation techniques these medical images have problems such as: 1. lack inherent spatial resolution; 2. contains different form of noise; 3. have boundary with the similar color intensity; and 4. populated with non-uniform illumination across the image and other imaging ambiguities. In many clinical studies, the segmentation process can be carried out either manually or automatically. Manual segmentation for the identification of several landmarks in medical images has been popularly considered, but is time consuming, tedious, error prone and observer-dependent. On the other hand, automatic segmentation technique are highly desirable because of its robustness, improved efficiency, reliability and faster computation. Therefore, the development of an automatic segmentation technique for the medical images has become an integral part of the medical diagnosis system that yields a practical insight. However, achieving a desirable result from automatic segmentation is still challenging. This is because; variation is seen in image features for different cases, even when produced with same imaging technique. The broad aim of this thesis is to identify the robust and automatic segmentation technique overcoming the issues seen in medical images and hence can assist doctors for the evaluation and detection of several pathologies. The objective is fulfilled by developing automatic segmentation algorithms and provide solutions to tackle challenges associated in two different imaging modalities: fundus photography (2D) and breast MRI (3D). The result is a series of work associated with the problem identification, analysis and a desirable solution with qualitative and quantitative validation. Specifically, we have strengthened the state-of-the-art by making the following novel contributions: 1. The analysis of retinal blood vessel is crucial for finding several pathological disorder that manifest through human eye. Therefore, blood vessel segmentation in fundus photography has great importance in medical image analysis. From the experiment, we observed that the retinal images with lesions, exudate’s, non-uniformed illuminations and pathological artefacts have intrinsic problems such as the absence of thin vessels and detection of false vessels. In our work, we developed an automatic blood vessel segmentation framework, which is effective in analysing retinal blood vessels on noisy, pathological and abnormal retinal images. Initially, the noise is minimized with image subtraction technique using morphological operation. Then, we investigated thin and thick blood vessels separately. Thin vessels are detected using local phase-preserving denoising, line detection, local normalization, and maximum entropy thresholding. Local phase-preservation denoising removes the additional noise while preserving phase information (detailed) of the image. Thick vessels are segmented using maximum entropy thresholding. The performance of the proposed methods is carried in four popular databases (DRIVE, STARE, CHASE DB1, HRF). The result shows that the proposed segmentation method is automatic, accurate and computationally efficient. Furthermore, the proposed methods is found to be superior when compared with the other methods in the state of art. 2. The automatic optic disc (OD) segmentation is a challenging task for the images, which are under the influence of noise, uneven illumination and pathologies. As per the state-of-art, development of OD segmentation is still a challenging task because of several reasons such as 1) Ophthalmic pathologies causes the change of color, shape or depth of OD 2) Retinal pathologies (exudate, lesion), sometimes possess similar properties causing a false identification of OD. 3) Different factors like illuminations and contrast irregularities, boundary artefacts and blurred image edges makes segmentation complicated and requires pixel to pixel analysis. 4) Also the texture feature of OD vary for different images, adding more challenges, thus requiring a pre-processing step prior to the segmentation. 5) If the vessels are dense and around OD, the identification the OD boundary becomes difficult. To solve the above-mentioned challenges, a new method for the accurate localization and detection of the optic disc is developed. The process utilizes kmeans clustering over foreground and background estimated images to obtain the brightest cluster. The obtained results are merged together to estimate the OD center. The OD boundary is then estimated using circular Hough transform (CHT) using the radius and center obtained in the initial step. The boundary estimation is also obtained from superpixels method. Finally, the OD boundary pixels are identified with the geometrical model over the edge information obtained from superpixels and CHT. The experiments carried out on seven publicly available database verify the efficiency of proposed methods. In addition, the outstanding results while compared with the other proposed methods in the current state of art proves the superiority of proposed methods. 3. A novel and accurate segmentation method of the breast region of interest (BROI) and breast density (BD) in breast MRI is proposed. The precise segmentation of BROI and BD is challenging, especially in noisy magnetic resonance images (MRI) due to similar intensity levels and the closely connected boundaries between BROI and other anatomical structure such as heart, lung and pectoral muscle. The segmentation of BROI is carried out in three major steps. Initially, we utilize adaptive wiener filtering and k-means clustering to denoised image by preserving edges and unwanted artefacts. Then, active contour based level sets is used to eliminate the heart area from the denoised image. Initial contour points for the active contour methods are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed to obtain a BROI segmentation by using a morphological operations and local adaptive thresholding methods. The segmentation of BD is obtained with 4 level fuzzy c-means (FCM) thresholding methods on the result image obtained from BROI segmentation. The validation of proposed methods is performed using the 1350 breast images from 15 female subjects. The obtained result show that the proposed method is automatic, fast and efficient. 4. The segmentation of breast lesions in breast MRI is considered as a important and challenging task in medical image analysis. Noise, intensity similarity of lesions and other tissues, and variable shape and size of lesion are the primary challenges during the process of lesion segmentation. Hence, the framework for the accurate segmentation of breast lesion from the DCE MRI image is proposed. The framework is built using max flow and min cut problems in the continuous domain over the denoised image. The proposed method is achieved in three steps. Firstly, in the pre-processing step, the post contrast and pre-contrast image are subtracted. This is followed by image registration that benefits by enhancing the tumor area. Secondly, a phase preservation denoising and pixel-wise adaptive Wiener filtering technique are used which is followed by max flow and min cut problems in the continuous domain. A denoising mechanism clears the noise in the image by preserving the useful and detailed features such as edges. Then, a tumor detection is done using continuous max flow. Finally, morphological operation is used as a post-processing step to further delineate the obtained results. The efficiency of the proposed method is verified with the series of qualitative and quantitative experiments carried out on 21 cases with two different MR image resolution. The obtained results when compared with the manually segmented results demonstrates the quality of segmentation obtained from the proposed method. The segmentation experiments for all above-mentioned four proposed algorithms are performed on Matlab R2013b running under Intel(R) core(TM) i5-4570s CPU@ 2.90 Ghz with 8GB of RAM. In an effort to test the performance of the proposed algorithms, both the public and private datasets with the manually drawn ground truth image are used. Moreover, the qualitative and quantitative measurements were used as a way to verify the robustness of the proposed algorithms. Also, the result were compared with the recent state-of-art which demonstrate the enhanced performance and advancement of the proposed methods. Finally, our overall results on the proposed methods show that the proposed algorithms are automatic, accurate and computationally efficient.

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