Sim Heng Ong

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentation algorithm to it. The k-means clustering is an unsupervised learning algorithm, while the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of 50 images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm

Autofocusing for tissue microscopy

Abstract This paper describes the implementation of autofocusing for tissue microscopy. We first investigate the suitability of several criterion functions for the evaluation of image sharpness. Since tissue sections are invariably stained, we also discuss the selection of the colour component on which autofocusing will be performed. In tissue microscopy, where a section generally comprises multiple layers, it is often not possible to obtain an image that is well focused over the field of view because of the limited depth of field of the objective. We describe focus enhancement algorithms, closely related to the autofocus system, which may be employed to obtain an entirely sharp image.

A rule-based approach for robust clump splitting

This paper presents a robust rule-based approach for the splitting of binary clumps that are formed by objects of diverse shapes and sizes. First, the deepest boundary pixels, i.e., the concavity pixels in a clump, are detected using a fast and accurate scheme. Next, concavity-based rules are applied to generate the candidate split lines that join pairs of concavity pixels. A figure of merit is used to determine the best split line from the set of candidate lines. Experimental results show that the proposed approach is robust and accurate.

Image analysis of tissue sections

The use of computers for the automated image analysis of tissue sections is becoming increasingly important. The paper presents an overview of current methodologies and summarizes developments in this field. A brief introduction followed by a survey is provided in each of these areas: image transformation, image segmentation and classification.

Segmentation of color images using a two-stage self-organizing network

Abstract We propose a two-stage hierarchical artificial neural network for the segmentation of color images based on the Kohonen self-organizing map (SOM). The first stage of the network employs a fixed-size two-dimensional feature map that captures the dominant colors of an image in an unsupervised mode. The second stage combines a variable-sized one-dimensional feature map and color merging to control the number of color clusters that is used for segmentation. A post-processing noise-filtering stage is applied to improve segmentation quality. Experiments confirm that the self-learning ability, fault tolerance and adaptability of the two-stage SOM lead to a good segmentation results.

A practical method for estimating fractal dimension

Abstract This paper describes a practical algorithm for estimating the fractal dimensions of textured images and discusses the scale limits for which it is applicable. The proposed method is an improvement over the differential box-counting method of Sarkar and Chaudhuri (1992, 1994). Computer generated image surfaces and natural textures are used to test our approach. The results confirm that our method is more accurate and efficient.

Tooth segmentation of dental study models using range images

The accurate segmentation of the teeth from the digitized representation of a dental study model is an important component in computer-based algorithms for orthodontic feature detection and measurement and in the simulation of orthodontic procedures such as tooth rearrangement. This paper presents an automated method for tooth segmentation from the three-dimensional (3-D) digitized image captured by a laser scanner. We avoid the complexity of directly processing 3-D mesh data by proposing the innovative idea of detecting features on two range images computed from the 3-D image. The dental arch is first obtained from the plan-view range image. Using the arch as the reference, a panoramic range image of the dental model can be computed. The interstices between the teeth are detected separately in the two range images, and results from both views are combined for a determination of interstice locations and orientations. Finally, the teeth are separated from the gums by delineating the gum margin. The algorithm was tested on 34 dental models representing a variety of malocclusions and was found to be robust and accurate.

Performance of coded OFDM in very shallow water channels and snapping shrimp noise

Although acoustic energy has been used effectively for point-to-point communications in deep-water channels, it has had limited success for horizontal transmissions in shallow water. Time-varying multipath propagation and non-Gaussian snapping shrimp noise are two of the major factors that limit acoustic communication performance in shallow water. Rapid time variation in the channel can limit the use of equalizers to compensate for frequency selective fading introduced due to multipath propagation. OFDM (orthogonal frequency division multiplexing), a communication technique widely used in wired and wireless systems, divides the available bandwidth across a number of smaller carriers, each of which experiences flat fading. This simplifies the equalizer structure and provides robustness against time-varying frequency-selective fading. Another source of signal degradation is impulsive noise from snapping shrimp, which affects several OFDM carriers at the same time. OFDM, when coupled with coding, can provide robustness against impulsive noise by distributing the energy for each bit over a longer period of time. We tested coded OFDM in a very shallow water channel in Singapore waters. The results show that it is a promising technique for use in very shallow, warm water channels

Level-set segmentation of brain tumors using a threshold-based speed function

The level set approach can be used as a powerful tool for 3D segmentation of a tumor to achieve an accurate estimation of its volume. A major challenge of such algorithms is to set the equation parameters, especially the speed function. In this paper, we introduce a threshold-based scheme that uses level sets for 3D tumor segmentation (TLS). In this scheme, the level set speed function is designed using a global threshold. This threshold is defined based on the idea of confidence interval and is iteratively updated throughout the evolution process. We propose two threshold-updating schemes, search-based and adaptive, that require different degrees of user involvement. TLS does not require explicit knowledge about the tumor and non-tumor density functions and can be implemented in an automatic or semi-automatic form depending on the complexity of the tumor shape. The proposed algorithm has been tested on magnetic resonance images of the head for tumor segmentation and its performance evaluated visually and quantitatively. The experimental results confirm the effectiveness of TLS and its superior performance when compared with a region-competition based method.