An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm

Sompong, Chaiyanan; Wongthanavasu, Sartra

doi:10.1016/j.eswa.2016.10.064

Cited by 35 publications

(15 citation statements)

References 21 publications

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“…In addition, we plan to integrate MedGA as a pre-processing step within an automatic pipeline defined in the context of MR image classification for efficient computer-assisted segmentation using thresholding techniques, such as (Ridler & Calvard, 1978;Trussell, 1979;Otsu, 1975). Indeed, MR image segmentation is a compelling task in radiology practice, for instance in brain tumor detection and delineation (Sompong & Wongthanavasu, 2017). Especially, we plan to apply MedGA to metastatic cancer segmentation in neuro-radiosurgery therapy (Leksell, 1949), wherein the enhancement region must be accurately segmented (Militello et al, 2015a;Rundo et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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Section: Discussionmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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“…CA have successfully been used in image processing, such as edge detection [41]- [44], noise filtering [40], [45]- [47]. saliency detection [48]- [50], image segmentation [51]- [53], and 3D image reconstruction [54]. For example, S. Wongthanavasu et al [41] proposed an edge detection method based on a cellular automata model.…”

Section: B Learning-based Dehazingmentioning

confidence: 99%

“…The method combined image color spatial feature weighting and the CA's self-iteration to speeds up the convergence of image segmentation. C. Sompong et al [51] proposed a Gray-level co-occurrence matrix based cellular automata (GLCM-CA) framework and Improved Tumor-Cut (ITC) algorithm to cope with ambiguous tumor boundaries on brain tumor segmentation task. The GLCM-CA transformed an original magnetic resonance (MR) image to the target featured image, while the ITC used a patch weighted distance to enhances the robustness of seed growing.…”

Section: B Learning-based Dehazingmentioning

confidence: 99%

Single Image Haze Removal Using Deep Cellular Automata Learning

Tangsakul

Wongthanavasu

2020

IEEE Access

Self Cite

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Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA's learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multilayer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.

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“…Sompong and Wongthanavasu presented the Improved Tumor‐Cut to cope with the robustness of seed growing in the standard Tumor‐Cut algorithm. The multimodal MRI brain tumor dataset BRATS 2013, including T1w, T2w, CE T1w, and FLAIR images, was processed.…”

Section: Related Workmentioning

confidence: 99%

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Rundo

Militello

Tangherloni

et al. 2017

Int J Imaging Syst Tech

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Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).

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An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm

Cited by 35 publications

References 21 publications

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Single Image Haze Removal Using Deep Cellular Automata Learning

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

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