Comparative Study on Histogram Equalization Techniques for Medical Image Enhancement

—Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is

This paper demonstrates the significance of histogram processing of an image particu larly the histogram equalization (HE). It is one of the widely used image enhancement technique. It has become a popular technique for contrast enhancement because the method is simple and effect ive. The basic idea of HE is to re-map the gray levels of an image. Here we propose two different techniques of Histogram Equalizat ion namely, the global HE and local HE. The Histogram Equalization has been performed in the MATLA B environ ment. The merits and demerits of both techniques of Histogram Equalization have also been discussed. It is seen after exhaustive experimentation on a nu mber of sample images that the proposed image enhancement techniques can be considered as an imp rovement over the inbuilt MATLAB function histeq.

An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images. The proposed approach starts with preprocessing the images to reduce the noise. Then, fusion rules are applied to get the fused image, and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region. Finally, a machine learning classifier classified the brain images into benign and malignant tumors. Computing statistical measures evaluate the classification potential of the proposed scheme. Experimental outcomes are provided, and the Enhanced Flower Pollination Algorithm (EFPA) system shows that it outperforms other brain tumor classification methods considered for comparison.

The magnetic resonance imaging (MRI) is very effective, yet non-invasive medical imaging technique for clinical diagnosis and for monitoring the abnormalities in neurological order. Currently, the analysis of microbleeds is performed by experienced neurologist based on their knowledge that is by scanning the image, detecting the black dots and identifying whether black dots are micro-bleeds or mimics. Medical image processing has made a great impact on medicine, diagnosis, and treatment. The most important part of image processing in medical is image segmentation. Watershed segmentation is an example of a robust technique for image segmentation. The common watershed transformation used for segmentation is the marker controlled segmentation. This conventional watershed algorithms for medical images is widespread because of its advantage to completely segment the medical images. However, the common drawback of watershed segmentation which is over-segmentation and its sensitivity to false edges segmentation. This paper introduces a new scheme of process to overcome the listed drawback by first applying clustering technique to produce the primary segmentation before applying the improved watershed algorithm. The uses of k-means algorithms which is unsupervised learning algorithms will help watershed segmentation to improve segmentation process. Then, the improved watershed segmentation used the level set thresholding based on gradient magnitude map to reduce the number of false edges and over segmentations. This proposed method demonstrates a significant improvement that may serves as a computer aided tools for radiologists in detecting microbleeds in MRI images.

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

Medical imaging like MRI and CT scan images are crucial for accurately diagnosing human brain disease. The traditional method for tumour analysis relies on the radiologist or physician visually inspecting the specimen, which can result in some incorrect classifications when a large number of MRI pictures need to be processed. An automated intelligent classification system is suggested that requires picture categorization in order to reduce human mistake rates. One of the illnesses that kills the majority of individuals worldwide is the brain tumour. If the tumour is accurately anticipated at an early stage, the likelihood that someone would survive can be increased. The human brain is studied using the magnetic resonance imaging (MRI) method to identify illnesses. In this project, Support Vector Machines (SVM)-based classification approaches are suggested and implemented to classify brain images; DWT will extract features from MRI images. The primary goal of this research is to provide a superior result, which is higher accuracy and reduced error rates for SVM-based MRI brain tumour prediction.

Objective To investigate the value of computed tomography (CT)-magnetic resonance imaging (MRI) image fusion in target volume delineation of postoperative precise radiotherapy for gliomas. Methods Thirty-six patients newly diagnosed with gliomas were enrolled and received postoperative intensity-modulated radiotherapy. Landmark (L) combined with manual fusion (M) and maximization of mutual information (MI) was used for image fusion. Target volume and organs at risk were delineated based on CT images and fused images, respectively. Comparison of the volume was made by pairwise t test. The distance from a landmark on CT image to the corresponding one on MRI image was calculated. The volume method and geometric center method were used to calculate the degree of volume overlap and changes in central positions after image fusion. Results The L+ M+ MI method achieved a high registration accuracy in image fusion, with a registration error less than 2 mm. In patients with grade Ⅲ-IV gliomas, the CT images yielded significantly smaller gross tumor volume (GTV) and clinical target volume (CTV) than the MRI images (74.62±46.91 vs. 105.99±58.86 cm3,P=0.042; 304.03±130.05 vs. 387.94±150.12 cm3,P=0.040). After image fusion, the smallest change in central position occurred in the chiasma (1.32±1.42 mm), and the largest change occurred in CTV (7.99±11.06 mm), followed by GTV and the brain stem. Conclusions CT-MRI image fusion helps to reduce the uncertainty of target volume delineation in patients with gliomas, especially in those with edema and residual tumor after surgery. Key words: Tomography, X-ray computed; Magnetic resonance imaging; Image fusion; Brain neoplasms/radiotherapy

In the last few years there has been rapid progress in the application of nuclear magnetic resonance clinical imaging as a diagnostic aid (Partain et al, 1983; Steiner, 1983; Worthington, 1983). Records kept by the operators of NMR imaging equipment indicate that about 1800 individuals have now been examined without incident within the United Kingdom. In view of this progress, an advisory group (Appendix) appointed by the National Radiological Protection Board recommends some modifications to the original advice given in 1981 (NRPB, 1981). The principal modifications are that: (1) It is no longer considered necessary to exclude volunteers with a history of epilepsy or cardiac disease. (2) It is not obligatory that a registered medical practitioner trained in resuscitation techniques be present during an exposure. (3) Higher limits are recommended for rates of change of magnetic flux density when the period of change is less than 10 ms. (4) The radiofrequency limit of 15 MHz has been removed. Theoretical considerations indicate that the interaction of an applied magnetic field with the intrinsic magnetic moments of protons and electrons within a molecule could alter its rate of chemical reaction (McLauchlan, 1981). Chemical reactions involving radical pair intermediates are expected to be sensitive to magnetic fields for this reason and a number of enzyme reactions that might be expected to be affected have been identified (A. Cass, personal communication). However, there is no experimental confirmation of these theoretical possibilities at present.

Alzheimer's disease (AD) is a kind of dementia characterized by memory loss in early stages. Damage to the nerve in the brain responsible for learning, reasoning, and remembering (a cognitive function) causes these symptoms. Treatment and patient care are most successful when they follow a timely and exact diagnosis. In this research, a Multi-Layered Dynamic- Feed Forward Neural Network (MLD-FFNN) method is used to analyse Magnetic resonance imaging (MRI) images to make accurate AD predictions. The approach has three essential components: image preprocessing, image segmentation, and classification. MRI datasets are used in the study to categorize subjects as non-demented, very mildly demented, mildly demented, or moderately demented. The initial step, data preprocessing, improves the quality of MRI images. Histogram equalization was designated as one of the methods discussed. Images' contrast may be improved with the use of technique called histogram equalization. Segmentation of images follows after preprocessing. Here, MRI images must be segmented into informative sections. The Gaussian filter (GF) method is used for image segmentation. When smoothing or filtering images, a method is used. Finally, a MLD-FFNN method is utilized for categorization. The segmented MRI scans are presumably the input for this neural network, which is trained to predict whether or not a patient has AD. This research shows effectiveness for early detection and treatment of AD because of its potential for precise prediction. Based on experimental results, this investigation achieved higher accuracy than other methods. The automated AD classification has the potential to serve as a useful aid for medical professionals in making diagnoses of AD and determining the best course of therapy.

Utilising Artificial Intelligence (AI) to Automate Defacing of the Nose in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images

We sought to examine the intra- and interobserver variability in measuring the cochlear duct length (CDL) from magnetic resonance imaging (MRI) images versus computed tomography (CT) images using an otological surgical planning software that uses measurements of the basal turn diameter and cochlear width to estimate the CDL. Twenty-one adult cochlear implant patients with preoperative MRI and CT images. Three fellowship-trained neurotologists served as the raters in the study. One rater measured the CDL using preoperative CT scans to serve as the benchmark. Two of the raters measured the CDL on preoperative MRI scans. One rater also remeasured the scans using MRI images after a period of 1 week to assess intraobserver variability. Intraclass correlational coefficients were calculated to assess for intra- and interobserver agreement. The mean CDL measured from the CT scans was 32.7 ± 2.0 mm (range 29.4 - 37.6 mm). The mean difference between the raters when measuring the CDL using MRI scans was -0.15 ± 2.1 mm (range -3.2 to 4.3 mm). The intraclass correlational coefficients for inter-rater reliability of CDL determination using MRI scans was judged as fair to excellent (0.68; 95% CI 0.41-0.84). The intrarater reliability of CDL determination using MRI scans was judged at fair to excellent (0.73; 95% CI 0.491-0.866). We demonstrate that a validated otological surgical planning software for estimating the CDL preoperatively had comparable performance using MRI scans versus the gold-standard CT scans.

Medical imaging techniques have widely revolutionized the diagnosis and treatment of various health conditions. Among these techniques, magnetic resonance imaging (MRI) has stood out as a noninvasive and versatile tool. Now, a breakthrough innovation called "manganese-carbon dots" is poised to enhance MRI imaging and provide physicians with even greater insight into the human body. In this study, one-pot hydrothermal method was used to fabricate magneto-fluorescent carbon quantum dots using manganese citrate, urea, and Mn2+. Manganese citrateAQ3 acted as a carbon source and contrast agent. TEM,XPS, FTIR, UV-Vis, fluorescent analysis confirmed the successful synthesis of magneto-fluorescent carbon quantum dots. The MTT assay was used to study its biocompatiblity, Finallay application of itscompound for mri imaging was investigated. Characterization Techniques confirmed the succesful synthesis of product. MTT assay showed no toxicity of this product on HEK-293 cells. In addition, it exhibited high r1 relaxivity (7.4 mM-1 S-1) suggesting excellent potential of magneto-fluorescent carbon quantum dots as MRI T1 contrast agent and enabling specific imaging. Based on the results obtained, the synthesized carbon quantum dots could be used as fluorescence/MRI bimodal platform for in vivo imaging.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.

In the last decades, image processing has become an interesting task in the field of medical imaging. one of the most important medical imaging techniques used for diagnosis is the magnetic resonance image (MRI) which is considered to be a very useful medical tool for detecting the tumor progression of multiple sclerosis MS. The segmentation of brain tumors from MRI images is an effective technique. Several recent approaches for segmentation and classification of MRI sequences have been proposed for the automatic detection of MS outliers. This paper illustrates an algorithm based on the meta-heuristic “Ant Colonies Optimization ACO” for the segmentation of MRI images. We propose to apply the technique of ant colonies to estimate the segmentations of brain MRI images from a novel MR datasets of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to optimize their overall rendering and to compare with consensual segmentation. The submitted algorithm is evaluated by using MATLAB GUI program.