This is to certify that I am responsible for the work presented in this project, that the original work is my own, except as specified in the references and acknowledgments, and that the original work contained herein was not undertaken or done by unspecified sources or persons . In this paper, we investigate the performance of two segmentation methods; level set, and texture-based, in prostate region segmentation. The level set method is one of the well-known partial differential equations (PDEs) based on image processing especially image segmentation as it relies on an initial value PDE for a spreading level set function.
Additionally, the level set method can perform numerical calculations involving curves and surfaces on a fixed Cartesian grid without parameterizing the object. Texture-based segmentation or factor texture segmentation method is introduced due to its ability to extract features from neighboring organs. By comparing the results of two different synthetic images, it showed that the texture-based method is preferable to level set because it can detect a smooth region boundary.
First of all, all praise to Allah S.W.T, for bestowing on me His blessings and strength to complete my five years of studies in Universiti Teknologi PETRONAS (UTP). With his permission and willingness, I am able to complete my Final Year Project (FYP) titled “Segmentation of MR Prostate Images”. I owe a great debt of gratitude to my family, especially my father, for his limit less support, both emotionally and financially.
I would also like to express my gratitude to my friends who gave me moral support to complete this project within the limited time.
Background of Study
Several types of Medical Imaging are done to look for possible spread of cancer. Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Computed Tomography (CT) and Transrectal Ultrasound (TRUS) are types of Medical Imaging which “use electromagnetic radiation to produce images of the body's internal structures human for the purpose of accurate diagnosis [1]. This process, being driven by the energy of the system and with all processes, may have kinetic limitations that prevent or hinder these rearrangements at low temperatures.
In the medical field, this process increases the sampling rate in the folding direction to segment a sufficiently accurate appearance of the object and its internal anatomical structures. The goal of segmentation in diagnostic imaging is particularly i) Prescribe the exact location of the tumor;. ii) Recover the 3D shape from the region of interest;. iii) Create a look that is a good approximation of the dataset and has some smoothness; and. iv). Since the segmentation process plays a key role in the preprocessing stage for an automatic cancer detection system, a trusted algorithm and method for evaluating the accuracy of synthetic and real data should be used in medical imaging.
The level set method is introduced in prostate segmentation as it can divide the image from the extended data set into multiple regions. In image segmentation, a local histogram is developed to solve the boundary localization problem and extract features from some local neighborhoods of phenomena.
Problem Statement
This process has broad applications in image separation of non-uniformly sampled data and automatic focusing of images. Points, curves and surface spots can be present in any data set, resulting in the construction of a weighted minimal surface-like model and the implementation of the vibration level set formulation along with optimal efficiency [3]. Segmentation using the Level Set method will be further developed and discussed for medical MRI images.
Segmentation using a factorial texture segment shows excellent results in segmenting textured images, which are generally more difficult to segment than non-textured images. In this work, a texture-based segmentation technique will be established and evaluated specifically for medical MRI images. Topology and deformation on the surface are also difficult to deal with due to the presence of noisy and highly non-uniform datasets.
Additionally, each person has a different body composition, which resulted in a different type of soft tissue information, shape, size, and texture. In this paper, the leveling method and texture-based technique will be presented for the segmentation of the prostate gland due to its ability to represent the boundaries of objects that change over time or are ill-defined.
Objective and Scope of Study
- Image Modality for Prostate Cancer Detection
- Image Segmentation in Medical Image Processing
- Application of Level Set Method in Image Processing
- Texture-Based in Segmentation
The consideration of the size of the data set and the linear system are important when designing an algorithm because there is no specific method that can solve the problem. Using other additional methods, interacting multi-iple model (IMM) estimator and probabilistic data association filter (PDAF), the filter's algorithm is applied to different prostate ultrasound images. The authors have proven the accuracy of the filter as it was able to converge to extract cavity contour details without selecting seed points inside the contours in a short time.
In addition, prostate volume assessment is also important because, in addition to diagnosis, prostate cancer activity can also be monitored. Without using an anatomical atlas, an algorithm called Inter-Slice Bidirectional Registratio n-based Segmentation (iBRS) took advantage of data redundancy between image slices in the volume data for prostate segmentation. Mathematical “methods in image processing, including the level set method, have been introduced since the advent of computed tomography in radiology.
A comparison between these two methods is made based on noise versions of synthetic images [13].” The level set method “can be used for segmentation from a range of data by providing a general framework for the deformation of appearances according to some rules. The resulting problem regarding the quantity on the interface to the neighborhood of the interface can be solved by the level set method.
Average curvature flow is one of the applications that using the discharge of normal velocity in a neighboring cap of the interface to move the interface. However, there is no particular research that proves the implementation of level set method for early detection of cancers [11]. More extensive study had made of the function and structure of the human brain, since the surface illustration of the cerebral cortex is crucial.
The author introduced the texton method to analyze changes in texture based on the structure of the same region. They proved that the accuracy of the designed technique is more accurate than other existing methods in texture segmentation activity. The performance evaluation of the developed model involves the comparison of 10 sets of synthesized texture images.
Research Methodology
The surface information is then measured by a suitable algorithm to calculate the ideal surface characteristics and optimization. Finally, the comparison of the quantitative accuracy performance of the image metrics will be calculated. The algorithm of the level set method is tested using synthetic data, as well as the image segmentation studies.
Instead of following the interface itself, the level set approach takes the original curve, as shown in Figure 3.2 (a), and embeds it into the surface. It intersects the x-y plane exactly where the curve lies, and the surface is called a set level function, 𝜑 because it accepts any point in the plane as input. Before the segmentation process, the image starts with positive, negative and zero values of the level adjustment function, 𝜑.
The result would be the final set-level function after the image, which undergoes boundary parameterization by iteration, as shown in Figure 3.3 (c). Based on the block diagram in Figure 3.4, the local spectral histogram algorithm will be applied to the input image as a localization process around the pixel. The texture uses a LOG filter to smooth out the edges of the image, giving a more accurate result in terms of boundary extraction.
The convolution of the image with LOG filter is represented by the function shown below. While H1 and H2 are the histograms of the left and right regions, and ω1 and ω2 are the weights. Using a clever technique called integral histogram, the calculation of local histogram can be done in a short time, regardless of the window size.
The features near the boundaries are then further analyzed for all features in the image. Due to the fact, this method uses the k-means clustering algorithm to transform the image into different regions [23]. The algorithm is applied to the synthetic image and the prostate image, as shown in Figure 3.5, to further understand the k-means clustering performance.
Project Key Milestones
Project Timeline
Level Set and Texture-Based Comparison
Theoretically, the cause of the defect is mainly due to the difficulty in finding the starting point of the region. The initialization phase in the level set plays a vital role as it helps to accurately detect the boundary of a region. Due to the complex appearance of the structure of the prostate images, it is quite difficult to delineate the border using the level determination method.
Instead of using initialization, texture-based method locates the boundary of a region using windowing. In this section, a non-textured image and a textured image are used to compare the performance of the two proposed methods. It is observed in Figure 4.2, the distinction between level set and texture based method is serious.
The texture-based technique, however, proved to be more reliable than the level set method, as it could extract the boundary of the synthetic image in Figure 4.2 (b). Since the surface of prostate images has a complex texture appearance, it is important to determine which method is suitable for the purpose of extraction.
Auto-Cropping
Quantitative Performance Approach
Five-year outcomes after prostatectomy or radiotherapy for prostate cancer: The Prostate Cancer Outcomes Study. Ladak, “Prostate boundary segmentation from 3D ultrasound images,” American Association of Physicists in Medicine, vol. Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Trans.
