Abdalla Mostafa

CT Liver Segmentation Using Artificial Bee Colony Optimisation

Abstract The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

Evaluating the effects of image filters in CT Liver CAD system

The main objective of image pre-processing is to improve the quality of an image so that it makes subsequent phases of image analysis like segmentation or recognition easier or more effective. Filtering is a key pre-processing technique used for various effects including contrast stretching, sharpening and smoothing. In this paper, we evaluate and analyse the effect of several image filtering techniques with respect to their computer aided diagnosis (CAD) performance. The techniques we investigate include contrast stretching, convolution, median fitlering, averaging, inverse transformation and logarithm transformation filters. An application of CT liver imaging CAD was chosen and the selected filters were applied to see their ability and accuracy to segment and isolate the liver region of interest using a region growing segmentation approach. The effect of the filtering techniques on the segmentation performance of the CAD system was investigated using mean squared error (MSE) and similarity index (SI). The highest performance was achieved for a contrast stretching filter (MSE = 0.1869, SI = 0.8423) and the combination of contrast stretching and average filter (MSE = 0.17198 and SI = 0.83257).

Liver segmentation in MRI images based on whale optimization algorithm

This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.

Nature Inspired Optimization Algorithms for CT Liver Segmentation

Nature inspired optimization algorithms have gained popularity in the last two decades due to their efficiency and flexibility when they applied to solve global optimization problems. These algorithms are inspired from the biological behavior by swarms of birds, fish and bees. In this chapter, we give an overview of some of nature inspired optimization algorithms such as Artificial Bee Colony (ABC), Cuckoo Search (CS), Social Spider Optimization (SSO) and Grey Wolf Optimization (GWO). Also, we present the usage of ABC and GWO algorithms for CT liver segmentation. The experimental results of the two selected algorithms show that the two algorithms are powerful and can obtain good results when applied to segment medical images.

Enhanced Region Growing Segmentation for CT Liver Images

This paper intends to enhance the image for the next usage of region growing technique for segmenting the region of liver away from other organs. The approach depends on a preprocessing phase to enhance the appearance of the boundaries of the liver. This is performed using contrast stretching and some morphological operations to prepare the image for next segmentation phase. The approach starts with combining Otsu’s global thresholding with dilation and erosion to remove image annotation and machine’s bed. The second step of image preparation is to connect ribs, and apply filters to enhance image and deepen liver boundaries. The combined filters are contrast stretching and texture filters. The last step is to use a simple region growing technique, which has low computational cost, but ignored for its low accuracy. The proposed approach is appropriate for many images, where liver could not be separated before, because of the similarity of the intensity with other close organs. A set of 44 images taken in pre-contrast phase, were used to test the approach. Validating the approach has been done using similarity index. The experimental results, show that the overall accuracy offered by the proposed approach results in 91.3 % accuracy.

Wolf Local Thresholding Approach for Liver Image Segmentation in CT Images

This paper enhances the usage of level set method to get a reliable liver image segmentation in CT images. The approach depends on a preprocessing phase to enhance the liver’s edges. This phase is performed in two ways using the morphological operations and wolf local thresholding. The first way starts with applying the morphological operations on the image to clean image annotation and bed lines. Then, it applies contrast stretching and texture filters. The other way applies the wolf local threshold to each point in the image. It uses a window or a mask to calculate the average and standard deviation to apply an iterative threshold. Each way is followed by a step of connecting ribs to separate the flesh and skin from liver’s region. The last step is to use level set method to segment the whole liver. A set of 47 images taken in pre-contrast phase, were used to test the approach. Validating the approach is done using similarity index measure. The obtained experimental results showed that the overall accuracy presented by the proposed approach results in 93.19 % accuracy for using morphological operations, and 93.30 % accuracy for using Wolf local thresholding.

Region Growing Segmentation with Iterative K-means for CT Liver Images

In this paper, it is intended to enhance the simple region growing technique (RG) to extract liver from the abdomen away from other organs in CT images. Iterative K-means clustering technique is used as a preprocessing step to pass the image to region growing and watershed segmentation techniques. The usage of K-means and region growing is preferred here for its simplicity and low cost of execution. The proposed approach starts with cleaning the annotation and enhancing the boundaries of the liver. This is performed using texture filter and ribs connection algorithm, followed by iterative K-means. K-means removes the clusters with higher intensity values. Then region growing is used to separate the whole liver. Finally, comes the role of watershed that divides the liver into a number of regions of interest (ROIs). The experimental results show that the overall accuracy offered by the proposed approach, results in 92.38% accuracy.

Artificial Bee Colony Based Segmentation for CT Liver Images

The objective of this paper is to evaluate an approach for CT liver image segmentation, to separate the liver, and segment it into a set of regions of interest (ROIs). The automated segmentation of liver is an essential phase in all liver diagnosis systems for different types of medical images. In this paper, the artificial bee colony optimization algorithm (ABC) aides to segment the whole liver. It is implemented as a clustering technique to achieve this mission. ABC calculates the centroid values of image clusters in CT images. Using the least distance between every pixel value and different centroids will result in a binary image for each cluster. Applying some morphological operations on every binary clustered image can help to remove small and thin objects. These objects represent parts of flesh tissues adjacent to the liver, sharp edges of other organs and tiny lesions spots inside the liver. This is followed by filling the large regions in each cluster binary image. Summation of the clusters’ binary images results in a reasonable image of segmented liver. Then, the segmented image of liver is enhanced using simple region growing technique (RG). Finally, one of ABC algorithm or watershed is applied once to extract the lesioned regions in the liver, which can be used by any classifier to determine the type of lesion. A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Testing the results is handled using similarity index to validate the success of the approach. The experimental results showed that the overall accuracy offered by the proposed approach, results in 93.73 % accuracy.

Artificial bee colony optimizer for historical Arabic manuscript images binarization

Historical manuscript image binarization is a very important step towards full word spotting system. In this paper, we present a novel binarization algorithm based on artificial bee colony optimizer. The proposed approach contains two phases. The first phase is stretching the intensity level of the image by contrast stretching filter and removing the noise by image cleaning algorithm, the second phase is determining the number of clusters, number of colony and iterations for starting Artificial Bee Colony (ABC) algorithm. The proposed approach is tested on a set of images collected from the electronic Arabic manuscripts database and compared against three famous binarization methods such as Niblacks, Otsus and Savouls. The Experimental results show that the proposed approach is a promising approach and can obtain the desired results better than the other compared methods.

Antlion Optimization Based Segmentation for MRI Liver Images

This paper proposes an approach for liver segmentation, depending on Antlion optimization algorithm. It is used as a clustering technique to accomplish the segmentation process in MRI images. Antlion optimization algorithm is combined with a statistical image of liver to segment the whole liver. The segmented region of liver is improved using some morphological operations. Then, mean shift clustering technique divides the segmented liver into a number of regions of interest (ROIs). Starting with Antlion algorithm, it calculates the values of different clusters in the image. A statistical image of liver is used to get the potential region that liver might exist in. Some pixels representing the required clusters are picked up to get the initial segmented liver. Then the segmented liver is enhanced using morphological operations. Finally, mean shift clustering technique divides the liver into different regions of interest. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSIM) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 94.49 % accuracy.