Detection and analysis of brain tumor from MRI by Integrated Thresholding and Morphological Process with Histogram based method

Islam, Md. Rezwanul; Imteaz, Md. Reezbhan; Marium-E-Jannat,

doi:10.1109/ic4me2.2018.8465663

Cited by 17 publications

(4 citation statements)

References 1 publication

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“…The morphological method is used to shrink, remove or fill in boundary pixels of an image. Threshold with the histogram-based approach of MRI also gives a better understanding [20]. Image quality is improved by median filters, used for the sorting networks and histogrambased methods [21].…”

Section: Medical Image Noisesmentioning

confidence: 99%

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Jayanthi

Sivakumar

2022

AEJ

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Medical images have noise, such as salt and pepper, speckle, and gaussian noise. So, getting the accuracy of magnetic resonance images is challenging always. The accuracy of brain images is essential for the clinical diagnosis process. The nonlinear method of median and morphological filters is used for enhancement, contrast adjustment, and histogram equalization for medical images and speech processing, to preserve them from noise and edge features. Because of its edge-preserving properties, this median spatial filter window is critically important and better for removing impulsive noise and speckle noise. Nine different shapes of windows and five different sizes of windows and five noise density injections are used for evaluating the performance of the median filter. The first person J.W.Tukey, did the smoothing process by median rank selection filter. The denoising using morphological process is also considered for analysis. These two nonlinear methods like median filtering and morphological methods are used for the restoration of noise from the original image. These two methods are applied in normal and seizure-affected images to calculate quality assessment metrics and also the performance evaluation metrics from magnetic resonance images. Any image needs to be denoised to get the accuracy, precision, and F-measure kind of common feature evaluation metrics. This paper evaluates two nonlinear filtering methods and their performance evaluated based on shapes and size of windows and for suppressing or de-noising to produce original noise-free images to assist clinically.

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Section: Medical Image Noisesmentioning

confidence: 99%

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Jayanthi

Sivakumar

2022

AEJ

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“…But how to select the threshold value is a big task, so optimal thresholding (otsu) is a better choice. The otsu thresholding is based on the variation of grayscale value in an image histogram [9]. To achieve the optimal threshold value for binarization or separation in between the foreground and background of an image, the maximum of difference in grayscale values of the two clusters (𝑉 𝑏 ) or of minimum difference within the grayscale values of clusters (𝑉 𝑤 ) are considered and shown in equations no.…”

Section: Thresholding and Morphological Operationsmentioning

confidence: 99%

Brain Tumor Segmentation & Classification using Optimized k-means (SFLA) and Ensemble Learning

2022

pnr

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Brain tumor is a common disease that can occur at any age in humans. Early-stage brain tumor segmentation and classification from lowcontrast MRI images is always difficult. In this paper, a new hybrid optimized k-means algorithm based on the shuffled frog leap algorithm (SFLA) followed by thresholding and morphological with ensemble learning is developed. The proposed work is divided into two segments. After pre-processing of low-contrast MRI images the brain tumor area is calculated from the segmented MRI image then the most efficient features are also extracted using discrete wavelet transformation (DWT) techniques. In the second segment, these extracted features are fed as input parameters into a trained brain tumor classifier using an ensemble learning approach. The ensemble-leaning approach model is trained by a feature dataset collected from an online source. The KNN, decision tree, gradient boosting, random forest, and ANN classifiers are used to classify the type of tumor (benign or malignant) from the low contrast brain tumor MRI image. The proposed framework is more efficient and has an accuracy (average of all models accuracy) of 98.07 percent, sensitivity of 98.21 percent, and specificity of 97.25 percent in predicting the type of brain tumor.

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“…The simplest segmentation approaches use a global threshold applied on pixel intensity to partition the original image: pixels with an intensity greater than threshold T are assigned to one region, while those below the threshold T are assigned to another one [20][21][22][23]. In this way, a binary image is created providing the segmentation of the original image with respect to the chosen threshold value.…”

Section: Split-and-merge Combined With Region Growingmentioning

confidence: 99%

On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

et al. 2021

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Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based—namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)—and distance-based metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% ± 6.50 (sFCM), JI = 65.90% ± 8.14 (sFCM), sensitivity = 77.84% ± 8.72 (FCM), specificity = 87.10% ± 8.24 (sFCM), FPR = 0.14 ± 0.12 (sFCM), and FNR = 0.22 ± 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 ± 0.90 (sFCM), MaxD = 4.04 ± 2.87 (sFCM), and HD = 2.21 ± 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.

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Detection and analysis of brain tumor from MRI by Integrated Thresholding and Morphological Process with Histogram based method

Cited by 17 publications

References 1 publication

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Brain Tumor Segmentation & Classification using Optimized k-means (SFLA) and Ensemble Learning

On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

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