Automatic Feature Set Selection for Merging Image Segmentation Results Using Fuzzy Clustering

The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms. As a consequence, a fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm was proposed that merged the initially segmented regions produced by a fuzzy clustering algorithm, using two different feature sets each comprising two features from pixel location, pixel intensity and a combination of both, which considered objects with similar surface variations (SSV), the arbitrariness of fuzzy c-means (FCM) algorithm using pixel location and the connectedness property of objects. The feature set selection for the initial segmentation in the merging technique was however, inaccurate because it did not consider all possible feature set combinations and also manually defined the threshold used to identify objects having SSV. To overcome these limitations, a new automatic feature set selection for merging image segmentation results using fuzzy clustering (AFMSF) algorithm is proposed, which considers the best feature set selection and also calculates the threshold based upon human visual perception. Both qualitative and quantitative analysis prove the superiority of AFMSF algorithm compared with other clustering techniques including FSSC, FCM, possibilistic c-means (PCM) and SFCM, for different image types

A multilevel image thresholding based on Hybrid Salp Swarm algorithm and Fuzzy Entropy

The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm

Signaling local non-credibility in an automatic segmentation pipeline

The advancing technology for automatic segmentation of medical images should be accompanied by techniques to inform the user of the local credibility of results. To the extent that this technology produces clinically acceptable segmentations for a significant fraction of cases, there is a risk that the clinician will assume every result is acceptable. In the less frequent case where segmentation fails, we are concerned that unless the user is alerted by the computer, she would still put the result to clinical use. By alerting the user to the location of a likely segmentation failure, we allow her to apply limited validation and editing resources where they are most needed. We propose an automated method to signal suspected non-credible regions of the segmentation, triggered by statistical outliers of the local image match function. We apply this test to m-rep segmentations of the bladder and prostate in CT images using a local image match computed by PCA on regional intensity quantile functions. We validate these results by correlating the non-credible regions with regions that have surface distance greater than 5.5mm to a reference segmentation for the bladder. A 6mm surface distance was used to validate the prostate results. Varying the outlier threshold level produced a receiver operating characteristic with area under the curve of 0.89 for the bladder and 0.92 for the prostate. Based on this preliminary result, our method has been able to predict local segmentation failures and shows potential for validation in an automatic segmentation pipeline

Automatic Optimum Atlas Selection for Multi-Atlas Image Segmentation using Joint Label Fusion

Multi-atlas image segmentation using label fusion is one of the most accurate state of the art image segmentation techniques available for biomedical imaging applications. Motivated to achieve higher image segmentation accuracy, reduce computational costs and a continuously increasing atlas data size, a robust framework for optimum selection of atlases for label fusion is vital. Although believed not to be critical for weighted label fusion techniques by some works (Sabuncu, M. R. et al., 2010, [1]), others have shown that appropriate atlas selection has several merits and can improve multi-atlas image segmentation accuracy (Aljabar et al., 2009, [2], Van de Velde et al., 2016) [27]. This thesis proposed an automatic Optimum Atlas Selection (OAS) framework pre-label fusion step that improved image segmentation performance dice similarity scores using Joint Label Fusion (JLF) implementation by Wang et al, 2013, [3, 26]. A selection criterion based on a global majority voting fusion output image similarity comparison score was employed to select an optimum number of atlases out of all available atlases to perform the label fusion step. The OAS framework led to observed significant improvement in aphasia stroke heads magnetic resonance (MR) images segmentation accuracy in leave-one out validation tests by 1.79% (p = 0.005520) and 0.5% (p = 0.000656) utilizing a set of 7 homogenous stroke and 19 inhomogeneous atlas datasets respectively. Further, using comparatively limited atlas data size (19 atlases) composed of normal and stroke head MR images, t-tests showed no statistical significant difference in image segmentation performance dice scores using the proposed OAS protocol compared to using known automatic Statistical Parametric Mapping (SPM) plus a touchup algorithm protocol [4] for image segmentation (p = 0.49417). Thus, leading to the conclusions that the proposed OAS framework is an effective and suitable atlas selection protocol for multi-atlas image segmentation that improves brain MR image segmentation accuracy. It is comparably in performance to known image segmentation algorithms and can lead to reduced computation costs in large atlas data sets. With regards to future work, efforts to increase atlas data size and use of a more robust approach for determining the optimum selection threshold value and corresponding number of atlases to perform label fusion process can be explored to enhance overall image segmentation accuracy. Furthermore, for an unbiased performance comparison of the proposed OAS framework to other image segmentation algorithms, truly manually segmented atlas ground truth MR images and labels are needed

Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells

BACKGROUND: Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. METHODS: A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. RESULTS: Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (P( C )), the false classification (P( f )), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. CONCLUSIONS: Computer simulations highlight that the proposed method substantially enhances the segmented image with smaller error rates better than other existing algorithms under the same settings (patterns and parameters). Moreover, it provides high classification accuracy, reaching the rate of 97.94%. Additionally, the segmentation method may be extended to other medical imaging types having similar properties

Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes

Information theoretic thresholding techniques based on particle swarm optimization.

In this dissertation, we discuss multi-level image thresholding techniques based on information theoretic entropies. In order to apply the correlation information of neighboring pixels of an image to obtain better segmentation results, we propose several multi-level thresholding models by using Gray-Level & Local-Average histogram (GLLA) and Gray-Level & Local-Variance histogram (GLLV). Firstly, a RGB color image thresholding model based on GLLA histogram and Tsallis-Havrda-Charv\u27at entropy is discussed. We validate the multi-level thresholding criterion function by using mathematical induction. For each component image, we assign the mean value from each thresholded class to obtain three segmented component images independently. Then we obtain the segmented color image by combining the three segmented component images. Secondly, we use the GLLV histogram to propose three novel entropic multi-level thresholding models based on Shannon entropy, R\u27enyi entropy and Tsallis-Havrda-Charv\u27at entropy respectively. Then we apply these models on the three components of a RGB color image to complete the RGB color image segmentation. An entropic thresholding model is mostly about searching for the optimal threshold values by maximizing or minimizing a criterion function. We apply particle swarm optimization (PSO) algorithm to search the optimal threshold values for all the models. We conduct the experiments extensively on The Berkeley Segmentation Dataset and Benchmark (BSDS300) and calculate the average four performance indices (Probability Rand Index, PRI, Global Consistency Error, GCE, Variation of Information, VOI and Boundary Displacement Error, BDE) to show the effectiveness and reasonability of the proposed models