A machine learning approach for brain image enhancement and segmentation

In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for surgical planning, etc. However, due to presence of noise and uncertainty between different tissues in the brain image, the segmentation of brain is a challenging task. This problem is rectified in this article using two stages. In the first stage an enhancement technique called contrast limited fuzzy adaptive histogram equalization (CLFAHE) which is a combination of CLAHE and fuzzy enhancement is used to improve the contrast of MRI Brain images. Contrast of the image is controlled using contrast intensification operator (Clip limit). The second stage deals with the segmentation of enhanced image. The enhanced brain images are segmented using new level‐set method which has the property of both local and global segmentation. Signed pressure force (SPF) function is also used here which stops the contours at weak and blurred edged efficiently.     

Brightness preserving bi‐level fuzzy histogram equalization for MRI brain image contrast enhancement

In this article, brightness preserving bi‐level fuzzy histogram equalization (BPFHE) is proposed for the contrast enhancement of MRI brain images. Histogram equalization (HE) is widely used for improving the contrast in digital images. As a result, such image creates side‐effects such as washed‐out appearance and false contouring due to the significant change in brightness. In order to overcome these problems, mean brightness preserving HE based techniques have been proposed. Generally, these methods partition the histogram of the original image into sub histograms and then independently equalize each sub‐histogram. The BPFHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two sub‐histograms based on the mean intensities of the multi‐peaks in the original image and then equalizes them independently to preserve image brightness. The quantitative and subjective enhancement of proposed BPBFHE algorithm is evaluated using two well known parameters like entropy or average information contents (AIC) and Feature Similarity Index Matrix (FSIM) for different gray scale images. The proposed method have been tested using several images and gives better visual quality as compared to the conventional methods. The simulation results show that the proposed method has better performance than the existing methods, and preserve the original brightness quite well, so that it is possible to be utilized in medical image diagnosis.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.     

一种基于FCM的图像分割方法

提出一种新的图像分割方法 FWFCM(fast walvet fuzzy C-means method),该方法对图像像素点的灰度进行模糊隶属度的分析,将需要聚类的像素空间投影到灰度直方图空间,从而减少了经典FCM算法的迭代计算量,提高了算法的收敛速度;并且利用小波变换的多分辨率的分析,抑制噪声点对图像分割的影响,提高了图像分割的精度.     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images

Histogram equalization is a well‐known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm (MHERCGA) is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 24–32, 2015     

Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images

Contrast limited fuzzy adaptive histogram equalization (CLFAHE) is proposed to improve the contrast of MRI Brain images. The proposed method consists of three stages. First, the gray level intensities are transformed into membership plane and membership plane is modified with Contrast intensification operator. In the second stage, the contrast limited adaptive histogram equalization is applied to the modified membership plane to prevent excessive enhancement in contrast by preserving the original brightness. Finally, membership plane is mapped back to the gray level intensities. The performance of proposed method is evaluated and compared with the existing methods in terms of qualitative measures such as entropy, PSNR, AMBE, and FSIM. The proposed method provides enhanced results by giving better contrast enhancement and preserving the local information of the original image. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 98–103, 2017     

Robust brain MRI denoising and segmentation using enhanced non‐local means algorithm

Image denoising is an integral component of many practical medical systems. Non‐local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non‐local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non‐local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c‐means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM‐filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1‐weighted (T1‐w) and T2‐weighted (T2‐w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2‐weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques.     

一种基于DA-GMRF的无监督图像分割方法

提出一种基于间断自适应高斯马尔可夫随机场(DA-GMRF)模型的无监督图像分割方法.针对MRF模型中的过平滑问题,利用边缘信息构造能量函数,定义了一种DA-GMRF模型.利用灰度直方图势函数自动确定分类数及分割阈值,进行多阈值分割,得到DA-GMRF模型中标记场的初始化,用Metroplis采样器算法进行标记场的优化,实现了图像的无监督分割.实验结果表明了该方法的有效性.     

Automatic Windowing for Highly Dynamic Industrial X-ray Image Based on Short-Term Energy of Gray Histogram

Removing the redundant information for 16-bit gray level images is not only beneficial to the observation of the region of interest to, but also one key step for image enhancement. In this paper, an automatic windowing algorithm is proposed for highly dynamic industrial X-ray image based on short-term energy of gray histogram. We first calculate values of the average energy of short-term frame histogram at high bit image, and then use a dual-threshold to detect the frames containing useful information. Finally, the endpoint gray values of the detected frames are regarded as the window endpoints, wherein the most appropriate frame length and frame shift are traversed and determined by comparing and searching the maximum of image contrast. The validity and practicability of the algorithm are analyzed qualitatively and quantitatively by a series of comparative experiments. The results show that the distance between gray bins is automatically stretched, and the image contrast is enhanced without changing the relationships between the pixels. In addition, the redundant background information is removed.     

Hierarchical fuzzy segmentation of brain MR images

In brain magnetic resonance (MR) images, image segmentation and 3D visualization are very useful tools for the diagnosis of abnormalities. Segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is the basic process for 3D visualization of brain MR images. Of the many algorithms, the fuzzy c‐means (FCM) technique has been widely used for segmentation of brain MR images. However, the FCM technique does not yield sufficient results under radio frequency (RF) nonuniformity. We propose a hierarchical FCM (HFCM), which provides good segmentation results under RF nonuniformity and does not require any parameter setting. We also generate Talairach templates of the brain that are deformed to 3D brain MR images. Using the deformed templates, only the cerebrum region is extracted from the 3D brain MR images. Then, the proposed HFCM partitions the cerebrum region into WM, GM, and CSF. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 115–125, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10035     

Hybrid method combining superpixel,supervised learning,and random walk for glioma segmentation

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.     

Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over‐segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation‐maximization (EM) algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225–232, 2016     

MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

In this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods.     

Brain tumor severity analysis using modified multi‐texton histogram and hybrid kernel SVM

Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi‐texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K‐cross fold validation method. From the results, we can conclude that the modified multi‐texton histogram with non‐linear kernels has shown the accuracy of 86% but the MTH with non‐linear kernels shows the accuracy of 83.8%.     

Modified transform-based gamma correction for MRI tumor image denoising and segmentation by optimized histon-based elephant herding algorithm

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low- and high-pass tetrolet coefficients. In the second module, multiple low-pass tetrolet coefficients are applied through a modified transform-based gamma correction method. Generalized cross-validation is used on multiple high-pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon-based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal-to-noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.     

二维直方图区域斜分Otsu阈值分割的快速迭代算法

鉴于现有的基于二维直方图区域直分的阈值选取方法中存在明显的错分,提出了一种新的二维直方图区域斜分方法,导出了基于二维直方图区域斜分的otsu法的快速迭代算法.在实验结果中给出了分割结果和运行时间,并与基于二维直方图直分的Otsu原始算法及其他两种快速算法进行了比较.结果表明所提出的快速迭代算法,使分割后的图像内部区域均匀,边界形状准确,有稳健的抗噪性,同时运行时间大幅减少.     

基于CAMSHIFT算法和形态学的人手动态分割

动态图象分割是计算机视觉分析中一个重要和困难的任务。该文讨论了一种基于CAMSHIFT算法和形态学的动态跟踪分割。首先把采集到的图象由RGB色彩空间转换到HSV色彩空间,因为肤色对HSV色彩空间的H色调具有恒常性。其后在HSV颜色空间建立肤色直方图模型,确定肤色概率度,运用CAMSHIFT算法对人手动态跟踪,然后运用形态学重构闭运算去除空洞和噪声。实验证明该方法能够有效的实现了人手的动态分割。     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016