Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques

Ananda, Resmi S.; Thomas, Tessamma

doi:10.1109/bmei.2012.6512995

Cited by 20 publications

(3 citation statements)

References 13 publications

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“…Penggunaan data latih yang sama untuk mengklasifikasikan sejumlah besar citra, dapat menyebabkan hasil yang bias. Teknik seperti ini yang membutuhkan banyak data latih dan data uji data relatif mempersulit proses (Ananda & Thomas, 2012).…”

Section: Pendahuluanunclassified

Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri

Akbar¹,

Rais²,

Sobari³

et al. 2019

jitk

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The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.

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Section: Pendahuluanunclassified

Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri

Akbar¹,

Rais²,

Sobari³

et al. 2019

jitk

View full text Add to dashboard Cite

show abstract

“…Another comparative study produced by Suhasini and Vijaykumar [45] compared the studies like Support Vector Machine Classifier, Fuzzy C Means [46], K-means [47] Hybrid Clustering [48], Mathematical Morphology [49] and Integrated Bayesian Model [50] and more. These various techniques were experimentally compared based on the accuracy.…”

Section: Comparative Studiesmentioning

confidence: 99%

Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey Brain Tumor Segmentation through Image Processing Methods: A Literature Survey

Zawish

Siyal

Shahani

et al. 2019

JBEMI

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Image segmentation is one of the most trending fields in the domain of digital image processing. For years, researchers have shown a remarkable progress in the field of Image Segmentation, precisely, for brain tumor extraction from various medical imaging modalities including X-Ray, Computed Tomography and most importantly, Magnetic Resonance Images (MRI). In these medical imaging modalities, accurate and reliable brain tumor segmentation is extremely imperative to perform safe diagnose, healthy treatment planning and consistent treatment outcome evaluation in order to understand and cure the complexities of chronic diseases such as Cancer. This paper presents various image processing techniques that are currently being used for brain tumor extraction from medical images. Though some great work has been done in this domain but none of the techniques has been widely accepted to be brought into practice in real time clinical analysis. The paper concludes with proposing some solutions that would aid in refining the results of the techniques which will lead to clinical acceptance of these computer aided methods.

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“…Tumor heterogeneity, which is not unusual in many applications, may make the boundary detection problem more challenging (Heppner, 1984;Ananda and Thomas, 2012). The BayesBD package allows us to address tumor heterogeneity by a repeated implementation.…”

Section: Real Data Applicationmentioning

confidence: 99%

BayesBD: An R Package for Bayesian Inference on Image Boundaries

Syring¹,

Li²

2017

The R Journal

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We present the BayesBD package providing Bayesian inference for boundaries of noisy images. The BayesBD package implements flexible Gaussian process priors indexed by the circle to recover the boundary in a binary or Gaussian noised image, with the benefits of guaranteed geometric restrictions on the estimated boundary, (nearly) minimax optimal and smoothness adaptive convergence rates, and convenient joint inferences under certain assumptions. The core sampling tasks for our model have linear complexity, and our implementation in c++ using packages Rcpp and RcppArmadillo is computationally efficient. Users can access the full functionality of the package in both Rgui and the corresponding shiny application. Additionally, the package includes numerous utility functions to aid users in data preparation and analysis of results. We compare BayesBD with selected existing packages using both simulations and real data applications, and demonstrate the excellent performance and flexibility of BayesBD even when the observation contains complicated structural information that may violate its assumptions.

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Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques

Cited by 20 publications

References 13 publications

Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri

Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri

Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey Brain Tumor Segmentation through Image Processing Methods: A Literature Survey

BayesBD: An R Package for Bayesian Inference on Image Boundaries

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