3D geometric split–merge segmentation of brain MRI datasets

Marras, Ioannis; Nikolaidis, Nikos; Pitas, Ioannis

doi:10.1016/j.compbiomed.2014.02.013

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Moreover, region-based techniques involve methods based on watershed [51], region growing and split-and-merge [52] algorithms. On the other hand, there are a number of software packages, which automatically perform a set of image processing routines such as bias field correction, skull splitting and automated segmentation.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension analysis of cerebellum in Chiari Malformation type I

Akar¹,

Kara²,

Akdemir³

et al. 2015

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

Fractal dimension analysis of cerebellum in Chiari Malformation type I

Akar¹,

Kara²,

Akdemir³

et al. 2015

Computers in Biology and Medicine

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“…When it comes to removing noise, the median filter outperforms linear filtering [54]. After that, the ground truth images are rehabilitated to binary image and region split, and merge segmentation [55] is applied to ensure precise brain tumor detection through RCNN. In region Split and merge Segmentation, similar regions are merged, and different regions are split until there are no more similar regions to merge, or different regions are left to split.…”

Section: Preprocessingmentioning

confidence: 99%

Brain Tumor Detection and Segmentation Using RCNN

Khan¹,

Shah²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors'. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

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“…Zhu et al [28] developed an approach to convert various types of 3D models into BREP (Boundary Representation) models and then perform model segmentation. Similarly, Marras et al [29] proposed a hybrid method, called the Adaptive Geometric Split Merge (AGSM) segmentation algorithm, which exploits both the region shape and data value characteristics to find the maximum homogeneity axis of the volume, and ultimately to divide the entire volume into several large homogeneous 3D regions.…”

Section: Ifc Extraction and Automated 3d Model Splitmentioning

confidence: 99%

Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

Wang

Rao

2020

Processes

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Background: With the ever-increasing availability of data and a higher level of automation and simulation, production scheduling in the factory for prefabrication can no longer be seen as an autonomous solution. Concepts such as building information modelling (BIM), graphic techniques, databases, and interface development as well as heightened emphasis on overall-process optimization topics increase the pressure to connect to and interact with interrelated tasks and procedures. Methods: The automated optimization framework detailed in this study intended to generate optimal schedule of prefabricated component production based on the manufacturing process model and genetic algorithm method. An extraction and segmentation approach based on industry foundation classes (IFC) for prefabricated component production is discussed. During this process, the position and geometric information of the prefabricated components are adjusted and output in the extracted IFC file. Then, the production process and the completion time of each process have been examined and simulated with the genetic algorithm. Lastly, the automated optimization solution can be formed by the linking production scheduling database and the computational environment. Results: This shows that the implementation of the automated optimization framework for the production scheduling of the prefabricated elements improves the operability and accuracy of the production process. Conclusions: Based on the integration technique discussed above, the data transmission and integration in the mating application program is achieved by linking the Python-based application, the Structured Query Language (SQL) database and the computational environment. The implementation of the automated optimization framework model enables BIM models to play a better foundational role in patching up the technical gaps between prefabricated building designers and element producers.

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3D geometric split–merge segmentation of brain MRI datasets

Cited by 4 publications

References 30 publications

Fractal dimension analysis of cerebellum in Chiari Malformation type I

Fractal dimension analysis of cerebellum in Chiari Malformation type I

Brain Tumor Detection and Segmentation Using RCNN

Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

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