MRI brain tumor segmentation: A forthright image processing approach

Khalid, Norlin; Ismail, Muhammad Firdaus; Manaf, Muhammad Azri Ab; Fadzil, Ahmad Firdaus Ahmad; Ibrahim, Shafaf

doi:10.11591/eei.v9i3.2063

Cited by 11 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CNN is widely used for removing noise from MRI and CT [19], [20]. The CNN-based noise removal techniques [21], [22], provide an effective way of eliminating a different kind of noise [23] from MRI and improving its reconstruction quality [24], [25], and enhancing segmentation [26], [27] and classification outcomes as well [28], [29]. The CNN employs a hierarchical learning mechanism where a feed-forward network is used for the extraction of different features and a hidden layer is used to optimize the feature learning weights.…”

Section: A Convolution Neural Networkmentioning

confidence: 99%

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Swetha¹,

Aditya²

2022

IJACSA

View full text Add to dashboard Cite

Medical Resonance Imaging (MRI) is nonradioactive-based medical imaging that provides a superresolution of tissues. However, because of its complex nature using existing Deep Learning-based noise removal (i.e., Denoising) techniques, the reconstruction quality is poor and time-consuming. An extensive study shows very limited work has been done on Brain Multiple Sclerosis (MS) Lesions MRI. Designing an efficient noise removal technique will aid in improving MRI quality; thereby will aid in achieving better segmentation classification performance. In reducing computing time and enhancing image quality (i.e. reduce noise) this paper presents the Sparse Feature Aware Noise Removal (SFANR) technique for Brain MRI using Convolution Neural Network (CNN) architecture. A sparse-aware feature is incorporated into the patch-wise morphology learning model for removing noise in large-scale MRI MS lesion datasets. Experimental results demonstrated that our model SFANR outperforms all other state-of-art noise removal techniques in terms of Peak-Signal-Noise-Ratio (PSNR), Structural Similarity Index Metric (SSIM) with less running time.

show abstract

Section: A Convolution Neural Networkmentioning

confidence: 99%

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Swetha¹,

Aditya²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Conventional image processing approach such as forthright image processing approach is considered as a classic technique. This approach segments the medical image manually by employing various image processing processes such as enhancement, morphology, and thresholding with different image would necessitating different segmentation parameters [9]. While this approach can return a competent segmentation performance, the approach in the end is impractical for large datasets as it requires great amount of time and effort to individually determine each image's segmentation parameters and to manually segment the images.…”

Section: Related Workmentioning

confidence: 99%

Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

Fadzil¹,

Khalid²,

Ibrahim³

2021

IJATEE

Self Cite

View full text Add to dashboard Cite

Medical images serve as a very important tool for medical diagnosis. Medical image segmentation is an area of image processing that segments critical parts of a medical image for diagnosis purposes. The emergence of machine learning approach for medical image segmentation specifically by employing Convolutional Neural Network (CNN) has become a ubiquity as other approaches does not able to compete with its robustness and accuracy. However, this approach is very exhaustive in terms of time and computing resources. The CNN approach mostly emphasizes on the spatial information regarding the image without using much of the individual data contained withing the image. Therefore, this paper proposed a method to amplify the pixel data of medical images via Object-oriented Programming (OOP) approach for segmentation using a straightforward sequential deep learning approach. The results indicated that the proposed method allows more than 90 % faster training time with 33.8 seconds average and overall better segmentation performance of 0.744 for balanced-accuracy metric compared to recent state-of-the-art CNN segmentation models such as SegNet and U-Net Models.

show abstract

“…The most important goals of computer-aided design (CAD) are for reliably recognizing images and extracting regions of interest (ROI) from images obtained from a variety of imaging modalities. These imaging modalities include computed tomography (CT) scans, X-rays, position emission tomography (PET), and magnetic resonance imaging (MRI) [18]- [27]. CAD systems are further subdivided into computer-aided detection (commonly abbreviated as CADe) and computer-aided diagnosis (CADd) (CADx).…”

Section: Introductionmentioning

confidence: 99%

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

Worldwide, lung cancer is the leading cause of mortality and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, support vector machine (SVM), naive bayes, K-nearest neighbor (KNN), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We anticipate that our study may aid professional researchers and radiologists in developing more effective CAD systems for detecting lung cancer.

show abstract

MRI brain tumor segmentation: A forthright image processing approach

Cited by 11 publications

References 0 publications

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging

Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

A review of convolutional neural network-based computer-aided lung nodule detection system

Contact Info

Product

Resources

About