Segmentation of brain tumors in MRI images using three-dimensional active contour without edge

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% +/- 4.7% compared with manual processes

Akurasi Klasifikasi Citra Digital Scenes RGB Menggunakan Model K-Nearest Neighbor dan Naive Bayes

Gambar citra digital merupakan larik (array) yang berisi nilai kompleks dengan bit tertentu yang dapat dihitung secara matetmatis. Pada penelitian ini ada dua metode yang digunakan untuk membandingkan hasil akurasi klasifikasi citra digital RGB yaitu dengan metode Naive Bayes dan K-Nearest Neighbor (KNN). Metode klasifikasi Naive Bayes berdasarkan perhitungan matematika probabilitas yang sederhana dan metode klasifikasi K-Nearest Neighbor (KNN) berdasarkan pada perhitungan kedekatan atau K. Kedua metode tersebut diberikan data set digital citra scenes RGB yang sama untuk proses pengelompokan dan pengklasifikasian selanjutnya data akan dilatih dan diuji untuk mendapatkan hasil akurasi. Berdasarkan klasifikasi data set citra digital RGB menggunakan metode K-Nearest Neighbor memiliki tingkat akurasi lebih besar dari metode Naive Bayes. Metode pemrosesan data citra digital menghasilkan Naive Bayes dengan akurasi yang diperoleh adalah sebesar 66% dan metode K- Nearest Neighbor yang diperoleh adalah dengan akurasi 76%. Dari analisis ini dapat disimpulkan bahwa metode K-Nearest Neighbor bekerja lebih baik dibandingkan dengan Naive Bayes untuk data citra digital RGB

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation

Automated brain tumour identification using magnetic resonance imaging:a systematic review and meta-analysis

BACKGROUND: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. METHODS: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. RESULTS: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had “good” (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. CONCLUSIONS: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18-99.94dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow

Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

The digital medical workflow faces many circumstances in which the images can be manipulated during viewing, extracting and exchanging. Reversible and imperceptible watermarking approaches have the potential to enhance trust within the medical imaging pipeline through ensuring the authenticity and integrity of the images to confirm that the changes can be detected and tracked. This study concentrates on the imperceptibility issue. Unlike reversibility, for which an objective assessment can be easily made, imperceptibility is a factor of human cognition that needs to be evaluated within the human context. By defining a perceptual boundary of detecting the modification, this study enables the formation of objective guidelines for the method of data encoding and level of image/pixel modification that translates to a specific watermark magnitude. This study implements a relative Visual Grading Analysis (VGA) evaluation of 117 brain MR images (8 original and 109 watermarked), modified by varying techniques and magnitude of image/pixel modification to determine where this perceptual boundary exists and relate the point at which change becomes noticeable to the objective measures of the image fidelity evaluation. The outcomes of the visual assessment were linked to the images Peak Signal to Noise Ratio (PSNR) values, thereby identifying the visual degradation threshold. The results suggest that, for watermarking applications, if a watermark is applied to the 512x512 pixel (16 bpp grayscale) images used in the study, a subsequent assessment of PSNR=82dB or greater would mean that there would be no reason to suspect that the watermark would be visually detectable. Keywords: Medical imaging; DICOM; Reversible Watermarking; Imperceptibility; Image Quality; Visual Grading Analysis

An automated system for the classification and segmentation of brain tumours in MRI images based on the modified grey level co-occurrence matrix

The development of an automated system for the classification and segmentation of brain tumours in MRI scans remains challenging due to high variability and complexity of the brain tumours. Visual examination of MRI scans to diagnose brain tumours is the accepted standard. However due to the large number of MRI slices that are produced for each patient this is becoming a time consuming and slow process that is also prone to errors. This study explores an automated system for the classification and segmentation of brain tumours in MRI scans based on texture feature extraction. The research investigates an appropriate technique for feature extraction and development of a three-dimensional segmentation method. This was achieved by the investigation and integration of several image processing methods that are related to texture features and segmentation of MRI brain scans. First, the MRI brain scans were pre-processed by image enhancement, intensity normalization, background segmentation and correcting the mid-sagittal plane (MSP) of the brain for any possible skewness in the patient’s head. Second, the texture features were extracted using modified grey level co-occurrence matrix (MGLCM) from T2-weighted (T2-w) MRI slices and classified into normal and abnormal using multi-layer perceptron neural network (MLP). The texture feature extraction method starts from the standpoint that the human brain structure is approximately symmetric around the MSP of the brain. The extracted features measure the degree of symmetry between the left and right hemispheres of the brain, which are used to detect the abnormalities in the brain. This will enable clinicians to reject the MRI brain scans of the patients who have normal brain quickly and focusing on those who have pathological brain features. Finally, the bounding 3D-boxes based genetic algorithm (BBBGA) was used to identify the location of the brain tumour and segments it automatically by using three-dimensional active contour without edge (3DACWE) method. The research was validated using two datasets; a real dataset that was collected from the MRI Unit in Al-Kadhimiya Teaching Hospital in Iraq in 2014 and the standard benchmark multimodal brain tumour segmentation (BRATS 2013) dataset. The experimental results on both datasets proved that the efficacy of the proposed system in the successful classification and segmentation of the brain tumours in MRI scans. The achieved classification accuracies were 97.8% for the collected dataset and 98.6% for the standard dataset. While the segmentation’s Dice scores were 89% for the collected dataset and 89.3% for the standard dataset