Factorization-based active contour segmentation and pelican optimization-based modified bidirectional long short-term memory for ovarian tumor detection

This paper proposes a novel three-dimensional convolution neural network-based modified bidirectional long short-term memory with pelican optimization (3D CNN based MBiLSTM with PO) algorithm for multiclass ovarian tumor detection. Initially, the International Collaboration on Cancer Reporting endometrial cancer dataset images are provided in pre-processing phase, which uses a pre-emphasis filter to process the input image. In the segmentation phase, pre-processed data is then partitioned into diverse subgroups (i.e., pixels), which minimizes the complexity of images. In this paper, a factorization-based active contour technique is employed in the effective segmentation of images. The segmented features are then extracted and classified using the 3D CNN-MBiLSTM with PO algorithm. Finally, the experimental results are conducted and compared with various other approaches for various performance metrics. Each metric is evaluated with respect to the different number of iterations. The accuracy, sensitivity, and specificity have reached a higher value of 98.5%, 96%, and 98.25%, respectively.     

Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from the segmented liver, extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features. In this work, the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering (FCM) which is one of the algorithms to segment the images. The dataset used in this method has been collected from various repositories and scan centers. The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency. It gives better results when compared with other existing algorithms. Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.     

MRI brain image classification—a hybrid approach

The present article proposes a novel computer‐aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi‐Class Support Vector Machine (MC‐SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K‐means algorithm called WFF‐K‐means and modified cuckoo search (MCS) and K‐means algorithm called MCS‐K‐means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K‐means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO‐K‐means, color converted K‐means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC‐SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226–244, 2015     

CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis

Brain tumor classification and retrieval system plays an important role in medical field. In this paper, an efficient Glioma Brain Tumor detection and its retrieval system is proposed. The proposed methodology consists of two modules as classification and retrieval. The classification modules are designed using preprocessing, feature extraction and tumor detection techniques using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The image enhancement can be achieved using Heuristic histogram equalization technique as preprocessing and further texture features as Local Ternary Pattern (LTP) features and Grey Level Co‐occurrence Matrix (GLCM) features are extracted from the enhanced image. These features are used to classify the brain image into normal and abnormal using CANFIS classifier. The tumor region in abnormal brain image is segmented using normalized graph cut segmentation algorithm. The retrieval module is used to retrieve the similar segmented tumor regions from the dataset for diagnosing the tumor region using Euclidean algorithm. The proposed Glioma Brain tumor classification methodology achieves 97.28% sensitivity, 98.16% specificity and 99.14% accuracy. The proposed retrieval system achieves 97.29% precision and 98.16% recall rate with respect to ground truth images.     

Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images

Segmentation of Brain tumor from the magnetic resonance imaging (MRI) of head scans is an essential requirement for clinical diagnosis since manual segmentation is a fatigue and time‐consuming process. Recent computer‐aided diagnosis systems depend on the development of fully automatic methods to overcome these problems. In the present work, a fully automated algorithm is proposed to extract and segment tumor regions from multimodal magnetic resonance imaging (MMMRI) sequences. The algorithm has three phases: (a) tumor portion extraction, (b) tumor substructure segmentation, and (c) 3D postprocessing. First, the algorithm extracts tumor portion using a set of image processing operations from T2, fluid‐attenuated inversion recovery (FLAIR), and T1C images. Here, the proposed modified fuzzy c means clustering algorithm is used for enhancing the tumor portion extraction process. Then, the substructures of tumor such as edema, enhancing tumor, and necrotic regions are segmented from MMMRI sequences, T2, FLAIR, and T1C using region‐wise set operations in Phase II. Finally, 3D visualization of the segmented tumor and volume estimation is performed as postprocessing in Phase III. The proposed work was experimented on BraTS 2013 dataset. The quantitative analysis is performed using William's Index, Dice, sensitivity, specificity, and accuracy and is compared with 19 state‐of‐the‐art methods. The proposed method yields comparable results as 77%, 53%, and 59% of Dice for complete, core, and enhancing tumor regions, respectively.     

Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t‐test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.     

Texton-based segmentation of retinal vessels

With improvements in fundus imaging technology and the increasing use of digital images in screening and diagnosis, the issue of automated analysis of retinal images is gaining more serious attention. We consider the problem of retinal vessel segmentation, a key issue in automated analysis of digital fundus images. We propose a texture-based vessel segmentation algorithm based on the notion of textons. Using a weak statistical learning approach, we construct textons for retinal vasculature by designing filters that are specifically tuned to the structural and photometric properties of retinal vessels. We evaluate the performance of the proposed approach using a standard database of retinal images. On the DRIVE data set, the proposed method produced an average performance of 0.9568 specificity at 0.7346 sensitivity. This compares well with the best-published results on the data set 0.9773 specificity at 0.7194 sensitivity [Proc. SPIE5370, 648 (2004)].     

Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K-means clustering

Liver and liver tumor segmentations are essential in computer-aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a fine-tuned SegNet model and the liver tumor was segmented using the K-means clustering method coupled with a power-law transformation-based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line TrackingAlgorithm

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

Brain tumor detection and diagnosis using ANFIS classifier

In this article, the segmented brain tumor region is diagnosed into mild, moderate, and severe case based on the presence of tumor cells in the brain components such as Gray Matter (GM), White Matter (WM), and cerebrospinal fluid (CSF). The modified spatial fuzzy c mean algorithm is used to segment brain tissues. The feature Local binary pattern is extracted from segmented tissues, which is trained and classified by ANFIS Classifier. The performance of the proposed brain tissues segmentation system is analyzed in terms of sensitivity, specificity, and accuracy with respect to manually segmented ground truth images. The severity of brain tumor is diagnosed into mild case if the segmented brain tumor is present in the grey matter. The severity of brain tumor is diagnosed into moderate case if the segmented brain tumor is present in the WM. The severity of brain tumor is diagnosed into severe case if the segmented brain tumor is present in the CSF region. The immediate surgery is required for severe case and medical treatment is preferred for mild and moderate case.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification

This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.     

A new rectangular window based image cropping method for generalization of brain neoplasm classification systems

Classification of brain neoplasm images is one of the most challenging research areas in the field of medical image processing. The main objective of this study is to design a brain neoplasm classification system that can be trained using multiple various sized MR images of different institutions. The proposed method is a generalized classification system; it can be used in a single institute or in a number of institutions at the same time, without any restriction on image size. The generalization and unbiased capability of the proposed method can bring researchers on a single platform to work on some standard forms of computer aided diagnosis system with more efficient diagnostic capabilities. In this study, a suitable size of moveable rectangular window is used between segmentation and feature extraction stages. A semiautomatic, localized region based active contour method is used for segmentation of brain neoplasm region. Discrete wavelet transform for feature extraction, principal component analysis for feature selection and support vector machine is used as classifier. For the first time MR images of 2 sizes and from different institutions are used in training and testing of brain neoplasm classifier. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23% and the maximum specificity of 93.93%. In addition, the proposed method is computationally less complex, requires shorter processing time and is more efficient in terms of storage capacity.     

A hybrid approach for oestrogen receptor status evaluation

Abstract

The aim of this work is to assist pathologists in the evaluation of tumour cells in microscopic breast images where we distinguish three kinds of cells: positive tumour cells for oestrogen receptor, negative tumour cells for oestrogen receptor, and non-tumour cells. This work has proven to be very difficult because of the variability of cells’ size, shape (morphology) and distribution. Conventional methods for segmentation like thresholding and edge detection are unable to resolve this problem. The herein proposed method is a hybrid approach combining segmentation and classification to ensure better results. While the morphological processes are used for artefact elimination and cell segmentation, the classification algorithm is used to automatically classify all existing cells in the image. The paper contains also a comparative study between fuzzy c-means clustering algorithm and neural network-based classification. The proposed approach was applied on several microscopic breast cancer cells images corresponding to eight patients. The experimental results are efficient and the found values are near to those announced by experts. To better interpret these results, we performed a statistical analysis in terms of sensitivity, specificity and accuracy of detected tumour cells. The statistics proved the efficacy of the proposed approach since a percentage exceeding 90% was recorded for sensitivity, specificity and accuracy for the totality of the studied images. When using neural networks, the statistics are slightly above those gathered with fuzzy c-means. We recorded over 97% for sensitivity, specificity and accuracy of detected cells, reaching an error rate below 3%. Furthermore, it should be kept in mind that analysing breast cells images using the proposed approach gives us important information such as number of tumour cells, and number and percentage of positive tumour cells. Moreover, it is so much less time-consuming than experts’ evaluation.     

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regularization control(DRC) method which is used for tablet packaging image segmentation.Since the intensities of tablet packaging images are inhomogenous,it is difficult to make image segmentation.Compared to methods based on level set,the proposed DRC method has some advantages for tablet packaging image segmentation.The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate.Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation.The developed penalty energy function,as regularization term,increases the constrained conditions based on the gradient flow conditions.Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended,the proposed method further improves the accuracy of image contours.Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy,and achieves better results for image contour segmentation compared to other level set methods.     

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.     

A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images

In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.     

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.