Techniques in Image Segmentations, its Limitations and Future Directions

There many techniques, used for image segmentation but few of them face problems like: improper utilization of spatial information. In this paper, combined fuzzy c-means algorithm (FCM) with modified Particle Swarm Optimization (PSO) to improve the search ability of PSO and to integrate spatial information into the membership function for clustering is used. Here, in this paper discussion on segmentation techniques with their limitations is done. This would help in determining image segmentation method which would result to improved accuracy and performance

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition

Development Of Techniques For The Detection Of Tumours In Breast Magnetic Resonance Imaging

Kanser payudara ialah penyebab utama kematian di kalangan pesakit kanser yang melanda wanita dan kanser kedua paling lazim di seluruh dunia. Pengimejan Resonans Magnetik (MRI) adalah salah satu daripada alat-alat radiologi yang paling berkesan untuk menyaring kanser payudara. Bagaimanapun, teknik-teknik pemprosesan imej diperlukan bagi membantu pakar radiologi dalam mentafsir imej dan memisahkan wilayah tumor bagi mengurangkan jumlah positif yang palsu. Dalam kajian ini, pendekatan segmentasi dengan ciri-ciri automatik dibangunkan untuk tumor MRI payudara. Kaedah bermula dengan pemerolehan data diikuti oleh proses prapemprosesan. Ini diikuti dengan proses pengecualian garis kulit payudara menggunakan kaedah bersepadu Level Set Active Contour and Morphological Thinning. Berikutnya, kesan penting dikesan menggunakan kaedah Mean Maximum Raw Thresholding (MMRT) dicadangkan. Kemudian, pada fasa segmentasi tumor, dua kaedah diubahsuai Seeded Region Growing (SRG) dicadangkan; iaitu Breast MRI Tumour menggunakan Modified Automatic SRG (BMRI-MASRG) dan Breast MRI Tumour menggunakan SRG berdasarkan Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). Data set MRI payudara RIDER digunakan untuk penilaian dan keputusan dibandingkan dengan data set sebenar (ground truth). Daripada analisis keputusan, dapat diperhatikan bahawa pendekatan yang dicadangkan mencatat hasil-hasil hasilan yang tinggi menerusi pelbagai langkah. Keputusan pengecualian garis kulit mencatat purata prestasi yang tinggi bagi kedua-dua peringkat peringkat segmentasi sempadan (kepekaan = 0.81 dan ketentuan = 0.94 dan peringkat penyingkiran kawasan kulit (kepekaan = 0.86 dan ketentuan = 0.97). Penilaian kualiti MMRT menunjuk keputusan lebih jitu dengan purata PSNR = 69.97 dan MSE = 0.01. Dalam fasa segmentasi tumor, keputusan-keputusan kepekaan untuk dua kaedah yang dicadangkan; BMRI-MASRG dan BMRI-SRGPSOC, menunjukkan hasil segmentasi yang lebih tepat dengan purata masing-masingnya 0.82 dan 0.84. Begitu juga, hasil ketentuan mencatat prestasi lebih baik berbanding dengan cara sebelumnya. Purata BMRI-MASRG dan BMRI-SRGPSOC adalah masing-masingnya 0.90 dan 0.91. ________________________________________________________________________________________________________________________ Breast cancer is the leading cause of death amongst cancer patients afflicting women and the second most common cancer around the world. Magnetic Resonance Imaging (MRI) is one of the most effective radiology tools to screen breast cancer. However, image processing techniques are needed to help radiologists in interpreting the images and segmenting tumours regions to reduce the number of false-positive. In this study, a segmentation approach with automatic features is developed for breast MRI tumours. The methodology starts with data acquisition followed by pre-processing. This is then followed with breast skin-line exclusion using integrated method of Level Set Active Contour and Morphological Thinning. Next, regions of interests are detected using proposed Mean Maximum Raw Thresholding method (MMRT). In the tumour segmentation phase, two modified Seeded Region Growing (SRG) methods are proposed; i.e. Breast MRI Tumour using Modified Automatic SRG (BMRI-MASRG) and Breast MRI Tumour using SRG based on Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). The RIDER breast MRI dataset was used for evaluation and the results are compared with the ground truth of the dataset. From analysing the evaluation results, it can be noticed that the proposed approaches scored high results using various measures comparing to previous methods. The results of skin-line exclusion scored high average performance in both stages; border segmentation stage (sensitivity = 0.81 and specificity = 0.94) and removal stage (sensitivity = 0.86 and specificity = 0.97). The quality evaluation of MMRT showed improved results with average of PSNR = 69.97 and MSE = 0.01. In the tumour segmentation phase, the sensitivity results of the two proposed methods; BMRI-MASRG and BMRI-SRGPSOC showed more accurate segmentation with averages of 0.82 and 0.84 respectively. Similarly, the specificity results also scored better performance compared to previous methods. The averages of BMRI-MASRG and BMRI-SRGPSOC are 0.90 and 0.91 respectively

Color Image Segmentation Using the Bee Algorithm in the Markovian Framework

This thesis presents color image segmentation as a vital step of image analysis in computer vision. A survey of the Markov Random Field (MRF) with four different implementation methods for its parameter estimation is provided. In addition, a survey of swarm intelligence and a number of swarm based algorithms are presented. The MRF model is used for color image segmentation in the framework. This thesis introduces a new image segmentation implementation that uses the bee algorithm as an optimization tool in the Markovian framework. The experiments show that the new proposed method performs faster than the existing implementation methods with about the same segmentation accuracy

Cuckoo lévy flight with otsu for image segmentation in cancer detection

Detecting cancer cells from computed tomography (CT), magnetic resonance imaging (MRI) or mammogram scan images is a challenging task as the images are black and white and the neighbouring organs tend to be separated by edges with smooth varying intensity. On top of that, medical images segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. A few bio-inspired algorithms were developed to efficiently generate optimum threshold values for the process of segmenting such images. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding, thus, this research is keen to solve the optimum threshold problems. This research propose an enhancement of image segmentation algorithms based on Otsu’s method by incorporating Cuckoo Search (CS) method for Lévy flight generation while simultaneously modifying and optimizing it to work on CT, MRI or mammogram image scanners, specifically to detect breast cancer. The performance of the proposed Otsu’s method with CS algorithm was compared with other bio-inspired algorithms such as Otsu with Particle Swarm Optimization (PSO) and Otsu with Darwinian Particle Swarm Optimization (DPSO). The experimental results were validated by measuring the peak signal-to-noise ratio (PNSR), mean squared error (MSE), feature similarity index (FSIM) and CPU running time for all cases investigated. The proposed Otsu’s method with CS algorithm experimental results achieved an average of 231.52 of MSE, 24.60 of PNSR, 0.93 of FSIM and 3.36 second of CPU running time. The method evolved to be more promising and computationally efficient for segmenting medical images. It is expected that the experiment results will benefit those in the areas of computer vision, remote sensing and image processing application

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Object Tracking in Video Images based on Image Segmentation and Pattern Matching

The moving object tracking in video pictures [1] has attracted a great deal of interest in computer vision. For object recognition, navigation systems and surveillance systems [10], object tracking is an indispensable first-step. We propose a novel algorithm for object tracking in video pictures, based on image segmentation and pattern matching [1]. With the image segmentation, we can detect all objects in images no matter whether they are moving or not. Using image segmentation results of successive frames, we exploit pattern matching in a simple feature space for tracking of the objects. Consequently, the proposed algorithm can be applied to multiple moving and still objects even in the case of a moving camera. We describe the algorithm in detail and perform simulation experiments on object tracking which verify the tracking algorithm‘s efficiency. VLSI implementation of the proposed algorithm is possible. The conventional approach to object tracking is based on the difference between the current image and the background image. However, algorithms based on the difference image cannot simultaneously detect still objects. Furthermore, they cannot be applied to the case of a moving camera. Algorithms including the camera motion information have been proposed previously, but, they still contain problems in separating the information from the background. The proposed algorithm, consisting of four stages i.e. image segmentation, feature extraction as well as object tracking and motion vector determination [12]. Here Image Segmentation is done in 3 ways and the efficiency of the tracking is compared in these three ways, the segmentation techniques used are ―Fuzzy C means clustering using Particle Swarm Optimization [5],[6],[17]”, ”Otsu’s global thresholding [16]”, ”Histogram based thresholding by manual threshold selection”, after image segmentation the features of each object are taken and Pattern Matching [10],[11],[20] algorithm is run on consecutive frames of video sequence, so that the pattern of extracted features is matched in the next frame , the motion of the object from reference frame to present frame is calculated in both X and Y directions, the mask is moved in the image accordingly, hence the moving object in the video sequences will be tracked

Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap