A survey on brain tumor detection using image processing techniques

Kapoor, Luxit; Thakur, Sanjeev

doi:10.1109/confluence.2017.7943218

Cited by 101 publications

(29 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The image enhancement depends upon different factors like computational time, computational cost, quality of the uncorrupted image, and the techniques used for noise elimination. Some linear and non-linear methods are used for the image preprocessing [2]. There are many techniques which are involved in this stage:…”

Section: B Image Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

2019

IJITEE

View full text Add to dashboard Cite

Digital image processing is a rising field for the investigation of complicated diseases such as brain tumor, breast cancer, kidney stones, lung cancer, ovarian cancer, and cervix cancer and so on. The recognition of the brain tumor is considered to be a very critical task. A number of approaches are used for the scanning of a particular body part like CT scan, X-rays, and Magnetic Resonance Image (MRI). These pictures are then examined by the surgeons for the removal of the problem. The main objective of examining these MRI images (mainly) is to extract the meaningful information with high accuracy. Machine Learning and Deep Learning algorithms are mainly used for analysing the medical images which can identify, localize and classify the brain tumor into sub categories, according to which the diagnosis would be done by the professionals. In this paper, we have discussed the different techniques that are used for tumor pre-processing, segmentation, localization, extraction of features and classification and summarize more than 30 contributions to this field. Also, we discussed the existing state-of-the-art, literature gaps, open challenges and future scope in this area.

show abstract

Section: B Image Preprocessingmentioning

confidence: 99%

“…Kapoor, L, et.al [29] discussed several techniques which are used in each phase of image processing. Pre-processing involves processes like noise reduction and noise removal, image reconstruction, conversion to grayscale, image enhancement.…”

Section: E Classificationmentioning

confidence: 99%

Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…2018 [8] Suggested a method for automatic segmentation and classification of MRI brain image with tumor. In this tumor region is extracted using Ostu's thresholding and morphological operations with 86% accuracy Aby Elsa Babu et al 2018 [10] Proposed a bilateral transformation method which is proved to be a good method for tumor detection in brain using image processing techniques Luxit Kapoor et al.2017 [11] In this paper he suggested about the numerous steps that are indulge in the detection of tumors, and prove that Segmentation is the most significant and propitious Miss. Shrutika Santosh et al 2017 [12] Proposed that brain tumor detection method and find extraction of boundaries of tumor by Sobel edge detection operator.…”

Section: Amruta Pramodmentioning

confidence: 99%

Detection of Brain Tumor using Image Processing Techniques

Kaur¹,

Singh²

2019

IJEAT

View full text Add to dashboard Cite

Image processing in biomedical field is being increasingly used for the detection and diagnosis of various abnormalities in the body parts. The detection of brain tumours using image processing on MRI images is one such field where better results are obtained as comparative to CT-scan and x-ray. Prior detection of the brain tumour is desirable and possible with the help of machine learning and image processing techniques. These techniques detect even a small abnormality in the human brain following a four-stage process which includes pre-processing, segmentation, feature extraction and optimization. Different parameters such as accuracy, PSNR, MSE are calculated to find out the efficiency of process and to compare it with other methods. This paper reviews about various different approaches which are used to detect the brain tumor using image processing techniques.

show abstract

“…ere are more than 120 types of tumors classied as benign or malignant (cancerous). Cancerous tumors cause serious health problems such as severe headaches, blindness, and paralysis [1]. e timely detection and diagnosis of these tumors at their very early stages help clinicians to decide the most appropriate treatment protocol.…”

Section: Introductionmentioning

confidence: 99%

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting

Nasor

Obaid

2020

International Journal of Biomedical Imaging

View full text Add to dashboard Cite

Brain tumors are a major health problem that a ect the lives of many people. ese tumors are classi ed as benign or cancerous. e latter can be fatal if not properly diagnosed and treated. erefore, the diagnosis of brain tumors at the early stages of their development can signi cantly improve the chances of patient's full recovery a er treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). e extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. e technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

show abstract

A survey on brain tumor detection using image processing techniques

Cited by 101 publications

References 8 publications

Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

Detection of Brain Tumor using Image Processing Techniques

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting

Contact Info

Product

Resources

About