Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method

Segmentation is an important aspect of medical image processing. For improving the accuracy in the detection of tumour and improving the speed of execution in segmentation, a new genetic-based genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method with back propagation neural network (BPNN) is proposed and presented in this paper. The proposed system consists of four steps: pre-processing, segmentation, feature extraction and classification. The GFSMRG method and its components, feature extraction and classification are explained in detail. The performance analysis of the GFSMRG method with respect to accuracy and time complexity are also discussed. The performance of this method has been validated both quantitatively and qualitatively by using the performance metrics such as Similarity Index, Jaccard Index, Sensitivity, Specificity and Accuracy.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture

Breast cancer is one of the deadly diseases in women that have raised the mortality rate of women. An accurate and early detection of breast cancer using mammogram images is still a complex task. Hence, this article proposes a novel breast cancer detection model, which included five major phases: (a) preprocessing, (b) segmentation, (c) feature extraction, (d) feature selection, and (e) classification. The input mammogram image is initially preprocessed using contrast limited adaptive histogram equalization (CLAHE) and median filtering. The preprocessed image is then subjected to segmentation via the region growing algorithm. Subsequently, geometric features, texture features and gradient features are extracted from the segmented image. Since the length of the feature vector is large, it is essential to select the optimal features. Here, the selection of optimal features is done by a hybrid optimization algorithm. Once the optimal features are selected, they are subjected to the classification process involving the neural network (NN) classifier. As a novelty, the weight of NN is selected optimally to enhance the accuracy of diagnosis (benign and malignant). The optimal feature selection as well as the weight optimization of NN is accomplished by merging the Lion algorithm (LA) and particle swarm optimization (PSO), named as velocity updated lion algorithm (VU‐LA). Finally, a performance‐based evaluation is carried out between VU‐LA and the existing models like, whale optimization algorithm (WOA), gray wolf optimization (GWO), firefly (FF), PSO, and LA.     

Brain segmentation in magnetic resonance human head scans using multi-seeded region growing

This paper presents a skull stripping method to segment the brain from MRI human head scans using multi-seeded region growing technique. The proposed method has two stages. In Stage-1, the brain in the middle slice is segmented, the brains in the remaining slices are segmented in Stage-2. In each stage, the proposed method is required to identify the rough brain mask. The fine brain region in the rough brain mask is segmented using multi-seeded region growing approach. The proposed method uses multiple seed points which are selected automatically based on the intensity profile of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) of the brain image. The proposed brain segmentation method using multi-seeded region growing (BSMRG) was validated using 100 volumes of T1, T2 and PD-weighted MR brain images obtained from Internet Brain Segmentation Repository (IBSR), LONI and Whole Brain Atlas (WBA). The best Dice (D) value of 0·971 and Jaccard (J) value of 0·944 were recorded by the proposed BSMRG method on IBSR dataset. For LONI dataset, the best values of D?=?0·979 and J?=?0·960 were obtained for the sagittal oriented images by the proposed method. The performance consistency of the proposed method was tested on the brain images of all types and orientation and have and produced better and stable results than the existing methods Brain Extraction Tool (BET), Brain Surface Extraction (BSE), Watershed Algorithm (WAT), Hybrid Watershed Algorithm (HWA) and Skull Stripping using Graph Cuts (GCUT).     

Fast image vector quantization using a modified competitive learning neural network approach

The basic goal of image compression through vector quantization (VQ) is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. The advantage of VQ image compression is its fast decompression by table lookup technique. However, the codebook supplied in advance may not handle the changing image statistics very well. The need for online codebook generation became apparent. The competitive learning neural network design has been used for vector quantization. However, its training time can be very long, and the number of output nodes is somewhat arbitrarily decided before the training starts. Our modified approach presents a fast codebook generation procedure by searching for an optimal number of output nodes evolutively. The results on two medical images show that this new approach reduces the training time considerably and still maintains good quality for recovered images. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 413–418, 1997     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

Threshold prediction for segmenting tumour from brain MRI scans

In recent decades, region growing methods in image segmentation plays a vital role in medical image processing. Nonetheless, the method needs more advancement to cope up with the images of current acquisition devices. This paper attempts to solve the problem of maintaining diversity among wide image specifications by optimizing the threshold. In order to accomplish this, we introduce a hybrid framework of Artificial Bee Colony and Genetic Algorithm in a region growing variant, in which gradient and intensity levels are used for segmentation. Eventually, the proposed work is subjected to classify the tumor and non‐tumor images, followed by the segmentation of tumor region in MRI images. Classification methodologies such as feed forward back propagation neural network, radial basis neural network, support vector machine with quadratic programming and adaptive neuro‐fuzzy inference system are considered for experimental investigation in which support vector machine with quadratic programming is found to be dominant than other methodologies. Proposed region growing method outperforms well on the classified image, when compared with the region growing variant and standard region growing method. The results are demonstrated with the aid of wide set of performance measures. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 129–137, 2014     

Fetal brain extraction from magnetic resonance image (MRI) of human fetus

Study of a fetus is a rapidly growing field of research and it requires fetal brain segmentation. Automatic segmentation of the fetal brain from magnetic resonance imaging (MRI) is challenging, due to the highly variable size and shape of the developing brain, possible brain structure abnormalities, movement of the fetus and a poor resolution of fetal MRI scans. This is in contrast to adult brain segmentation, where the brain structure is stable and several established methods exist. This paper presents a fully automatic segmentation method to segment the fetal brain portion from MRI. The segmentation pipeline developed in this study includes contrast enhancement, region growing and hole filling. Twenty-five volumes of retrospective fetal MRI are used in this work. Experimental results show that this method can successfully segment the fetal brain from magnetic resonance images which are comparable to that of a semi-automatic method.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

综合边缘检测和区域生长的红外图像分割方法

针对红外图像的特点,提出了一种综合应用边缘检测和区域生长方法的图像分割方法。其思路为:先对图像进行边缘提取,得到边缘像素点集;然后利用该点集的平均灰度和目标区域的连通性作为生长判决条件,采用区域生长法实现图像分割。仿真结果表明,该方法能快速准确有效地实现红外图像分割,避免了单独使用边缘提取或区域生长法进行图像分割时的典型分割错误。     

Mammographic mass detection by adaptive thresholding and region growing

We present an efficient method to detect mass lesions on digitized mammograms, which consists of breast region extraction, region partitioning, automatic seed selection, segmentation by region growing, feature extraction, and neural network classification. The method partitions the breast region into a fat region, a fatty and glandular region, and a dense region, so that different threshold values can be applied to each partitioned region during processes of the seed selection and segmentation. The mammographic masses are classified by using four features representing shape, density, and margin of the segmented regions. The method detects subtle mass lesions with various contrast ranges and can facilitate a procedure of mass detection in computer‐aided diagnosis systems. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 340–346, 2000     

基于神经网络的玻璃缺陷声学检测方法

描述了一种通过声学信号检测玻璃制品缺陷的方法。在实现步骤上,首先采集了不同缺陷类型的玻璃瓶敲击声,然后经过频谱变换及小波包变换,将敲击信号映射至不同的变换域中,并在每个变换域中提取信号的特征,从而将样本的缺陷信息对应为统计特征和物理特征,并采用基于互信息量的特征选择算法对特征空间进行降维;降维后的特征子集作为后向传播神经网络的输入参数,再由该神经网络实现对玻璃缺陷的自动化检测。结果表明,在已有实验样本数据下,该缺陷检测算法能准确高效地检测出存在缺陷的样本,识别结果的F-值稳定在95%左右。     

基于神经网络和方差的快速分形图像编码算法

为克服快速分形图像编码带来的解码图像质量下降问题,提出了一种神经网络与方差混合编码的快速分形图像编码算法.该算法结合图像子块复杂度与方差值的对应关系,根据每个区块的方差值大小选择适当的映射编码方法,即对于方差值相对小的区块采用方差编码以提高编码速度,对于方差值相对大的区块采用神经网络编码以提高编码质量.该算法可以较好地修正传统分形编码中由于自仿射映射结构限制所带来的解码质量偏低的问题,在大幅提高编码速度的同时,很好地保持了图像的编码质量.实验结果表明,该算法对比基本分形编码算法可以加速24倍,解码图像的质量对比方差快速分形编码算法有1.1dB的提高.同时,该算法的硬件实现比较容易,非常贴近实用化.     

PCNN与形态匹配增强相结合的视网膜血管分割

针对人工手动提取视网膜血管工作量大,主观性强等问题,本文提出了一种将区域生长思想、脉冲耦合神经网络(PCNN)、高斯滤波器组及Gabor滤波器相结合的视网膜血管分割方法。首先将二维高斯滤波器组、二维Gabor匹配滤波器相结合,对视网膜血管区域进行形态匹配增强,提升血管与背景的对比度。然后将带有快速连接机制的PCNN与区域生长思想相结合,每次从未处理的像素点中选取亮度最大的作为种子,使用自适应的连接系数及停止条件,实现眼底图像中血管的自动分割。整个算法在DRIVE眼底数据库上的实验结果显示,平均准确度、灵敏度、特异性分别达到93.96%、78.64%、95.64%,分割结果中血管断点少,微小血管清晰,具有较好的应用前景。     

基于卷积神经网络和Transformer网络的鸟声识别

针对传统鸟声识别算法中特征提取方式单一、分类识别准确率低等问题,提出一种结合卷积神经网络和Transformer网络的鸟声识别方法。该方法综合考虑网络局部特征学习和全局上下文依赖性构造,从原始鸟声音频信号中提取短时傅里叶变换(Short Time Fourier Transform,STFT)语谱图特征,将其输入到卷积神经网络(ConvolutionalNeural Network,CNN)中提取局部频谱特征信息,同时提取鸟声信号的对数梅尔特征及一阶差分、二阶差分特征用于合成梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)混合特征向量,将其输入到Transformer网络中获取全局序列特征信息,最后融合所提取的特征可得到更丰富的鸟声特征参数,通过Softmax分类器得到鸟声识别结果。在Birdsdata和xeno-canto鸟声数据集上进行实验,平均识别准确率分别达到了97.81%和89.47%。实验结果表明该方法相较于其他现有的鸟声识别模型具有更高的识别准确率。     

Automatic vision-based grain optimization and analysis of multi-crystalline solar wafers using hierarchical region growing

Solar power has become an attractive alternative source of energy. The multi-crystalline solar cell has been widely accepted in the market because it has a relatively low manufacturing cost. Multi-crystalline solar wafers with larger grain sizes and fewer grain boundaries are higher quality and convert energy more efficiently than mono-crystalline solar cells. In this article, a new image processing method is proposed for assessing the wafer quality. An adaptive segmentation algorithm based on region growing is developed to separate the closed regions of individual grains. Using the proposed method, the shape and size of each grain in the wafer image can be precisely evaluated. Two measures of average grain size are taken from the literature and modified to estimate the average grain size. The resulting average grain size estimate dictates the quality of the crystalline solar wafers and can be considered a viable quantitative indicator of conversion efficiency.     

A fully automatic methodology for MRI brain tumour detection and segmentation

In this paper, a complete and fully automatic MRI brain tumour detection and segmentation methodology is presented as an efficient clinical-aided tool using Gaussian mixture model, Fuzzy C-Means, active contour, wavelet transform and entropy segmentation methods. The proposed algorithm is based on two main parts: the skull stripping and tumour auto-detection and segmentation. The first part was evaluated using IBSR, LPBA40 and OASIS databases, and the obtained results show that our proposed method outclasses the best popular algorithms of brain extraction with scores of 0.913, 0.954 and 0.957 for the Jaccard index, Dice coefficient and sensitivity, respectively. The second part has been evaluated using BRATS database; this methodology has achieved an accuracy of 69% of true detection, and a false detection is around 22% of healthy cases detected as tumour cases and a false detection is around 9% of tumour cases detected as healthy cases. So, the tumour segmentation performed 0.67 Jaccard index and 0.69 Dice coefficient. Our methodology is found to be a fast, effective, accurate and fully automatic one without the need to any human interaction and prior knowledge for training phases as supervised methodologies in clinical applications.     

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.     

Fusion of MRI and CT images using guided image filter and image statistics

In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.     

Compatible abnormality detection technique for CT and MRI brain images

Abstract

An automated computerised tomography (CT) and magnetic resonance imaging (MRI) brain images are used to perform an efficient classification. The proposed technique consists of three stages, namely, pre-processing, feature extraction and classification. Initially, pre-processing is performed to remove the noise from the medical MRI images. Then, in the feature extraction stage, the features that are related with MRI and CT images are extracted and these extracted features which are given to the Feed Forward Back-propagation Neural Network (FFBNN) is exploited in order to classify the brain MRI and CT images into two types: normal and abnormal. The FFBNN is well trained by the extracted features and uses the unknown medical brain MRI images for classification in order to achieve better classification performance. The proposed method is validated by various MRI and CT scan images. A classification with an accomplishment of 96% and 70% has been obtained by the proposed FFBNN classifier. This achievement shows the effectiveness of the proposed brain image classification technique when compared with other recent research works.