CT Scan Based Brain Tumor Recognition and Extraction using Prewitt and Morphological Dilation

Soni, Akanksha; Rai, Avinash

doi:10.1109/iccci50826.2021.9402677

Cited by 8 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, when identifying a brain tumor within an MRI image, it is necessary to enlarge and isolate it from the surrounding background. To achieve this, the morphological morphology operator is preferred over the median filter [37] as it yields better results for MRI imaging. Let I,  2 B represents the binary input image or structuring element.…”

Section: ) U-net and U-net++dsm Modelmentioning

confidence: 99%

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

View full text Add to dashboard Cite

The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be timeconsuming and delay diagnosis. To address this challenge, researchers have set out to develop an algorithm that can automatically determine whether MRI images contain brain tumors and identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. To enhance the segmentation power of U-Net++DSM, medical professionals have trained a dilation operator using fully annotated images. The results of this method demonstrate that the combination of U-Net++DSM and the dilation operator significantly improves segmentation accuracy, especially when the number of fully-labeled images is limited. The results show that the proposed U-Net++DSM outperforms traditional U-Net models by achieving high segmentation performance, surpassing other state-of-the-art models, with a sensitivity of 98.59.00%, a specificity of 98.72%, an accuracy of 98.64%, and an average Dice score of 98.81% when tested on publicly available databases. Compared to other existing segmentation methods, the U-Net++DSM method has the potential to yield even better brain tumor segmentation results in terms of pixel-based classification and dice similarity performance metrics.

show abstract

Section: ) U-net and U-net++dsm Modelmentioning

confidence: 99%

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The image extraction process aims to separate the ROI area from the background, so as to obtain information and distinguishing characteristics of the object to be analyzed [24,25]. The obstacle in the image extraction process is how to separate the desired object from several objects in the image that have the same intensity value, especially in this study the many characteristics of the object and overlapping in the Temporal bone image with the same intensity value.…”

Section: Image Extractionmentioning

confidence: 99%

Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

Arlis

Defit

Sumijan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

Mastoiditis is disease that to infection of the mastoid bone cavity that affects the size of the air cell system of the temporal bone. Visually, the information temporal CT image mastoid bone has can assist medical experts in viewing the mastoid air cell system (MACS), but the fact that medical personnel are experiencing difficulties in determining the size MACS is due to the many different characteristics and objects overlap, so that in the measurement of the area, precise and accurate results have not been obtained. This study aims to separate the object of the MACS with the development of extraction. The proposed method uses Morphology and Regionprops operations. The dataset used in the testing process is 347 of 5 patients indicated for Mastoiditis. The results obtained can calculate the area of MACS for each test image. Based on image testing, the area of the smallest MACS in this study was 0.589 cm2 and the largest was 6.183 cm2. This, the smaller the size of the MACS indicates the severity of infection, so this study can help medical personnel make decisions and take appropriate treatment actions.

show abstract

An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

Badshah

Farhatullah

Khan

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

CT Scan Based Brain Tumor Recognition and Extraction using Prewitt and Morphological Dilation

Cited by 8 publications

References 5 publications

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

Contact Info

Product

Resources

About