scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

scholarly journals Reconstruction of Three-Dimensional Geometry of the Vessels by Computed Tomography Data

2019 ◽  
Vol 17 (3) ◽  
pp. 5-17
Author(s):  
V. V. Borisenko ◽  
N. S. Serova ◽  
A. M. Chepovskiy
Keyword(s):  
Three Dimensional ◽  
Internal Surface ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Tetrahedral Mesh ◽  
Second Stage ◽  
Seeded Region Growing ◽  
3D Space ◽  
Voxel Model ◽  
Isosurface Reconstruction

We consider algorithms of 3D reconstruction for the internal surface of cardiac vessels. The precise reconstruction of vessel geometry is necessary for the creating a hydrodynamic model of blood supply for the heart and computing various parameters of blood flow. To compute a triangulation of blood vessel walls, we use the combination of two methods. At the first stage we apply the 3D seeded region growing algorithm to reconstruct a set of voxels inside vessels. At the second stage we use the isosurface reconstruction algorithm based on the tessellation of 3D space into small tetrahedral cells. We use the tetrahedral mesh, which was proposed in the works of S. Chan, E. Purisima (1998), and V. Skala (2000). Tetrahedra in this mesh are constructed on common faces of adjacent cubes in a cubic lattice, so it fits well with the voxel model. The mesh is constructed only in the neighborhood of the border of voxel set obtained at the first stage as the result of seeded region growing algorithms.

scholarly journals Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing

2008 ◽  
Author(s):  
Yingyi Qi ◽  
Wei Xiong ◽  
Wee Keng Leow ◽  
Qi Tian ◽  
Jiayin Zhou ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Region Growing ◽  
Ct Scans ◽  
Tumor Segmentation ◽  
Morphological Operations ◽  
Model Matching ◽  
Bayesian Decision ◽  
Seeded Region Growing

Automatic segmentation of liver tumorous regions often fails due to high noise and large variance of tumors. In this work, a semi-automatic algorithm is proposed to segment liver tumors from computed tomography (CT) images. To cope with the variance of tumors, their intensity probability density functions (PDF) are modeled as a bag of Gaussians unlike the previous works where the tumor is modeled as a single Gaussian, and employ a three-dimensional seeded region growing (SRG) method. The bag of Gaussians are initialized at manually selected seeds and updated during growing process iteratively. There are two criteria to be fulfilled for growing: one is the Bayesian decision rule, and the other is a model matching measure. Once the growing is terminated, morphological operations are performed to refine the result. This method, showing promising performance, has been evaluated using ten CT scans of livers with twenty tumors provided by the organizer of the 3D Liver Tumor Segmentation Challenge 2008.

An improved seeded region growing algorithm

1997 ◽  
Vol 18 (10) ◽  
pp. 1065-1071 ◽  
Author(s):  
Andrew Mehnert ◽  
Paul Jackway
Keyword(s):  
Region Growing ◽  
Seeded Region Growing

scholarly journals Preoperative virtual simulation for synchronous multiple primary lung cancers using three-dimensional computed tomography lung reconstruction: a case report

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Weichun Wu ◽  
Yimin Wu ◽  
Gang Shen ◽  
Guofei Zhang
Keyword(s):  
Computed Tomography ◽  
Thoracoscopic Surgery ◽  
Three Dimensional ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Term Survival ◽  
Lung Cancers ◽  
Multiple Primary ◽  
Feasible Alternative

Abstract Background As the positions and sizes of nodules in synchronous multiple primary lung cancer (SMPLC) patients differ, the development of surgical strategies to maximize long-term survival and preserved postoperative pulmonary function in SMPLC patients for whom surgical resection is an alternative strategy presents challenges. Case presentation We provide a case managed through video-assisted thoracoscopic surgery (VATS) resection using three-dimensional computed tomography lung reconstruction (3D-CTLR) to reconstruct lobes containing pulmonary nodules to preoperatively simulate and intraoperatively guide the extent and method of resection. Conclusion The successful attempt demonstrates a technically simplified, feasible alternative to preoperative plans utilizing less invasive VATS to manage SMPLC.

Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images

2000 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Omatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules

The Helium Stockpile: A Collaboration in Mathematical Folding Sculpture

Leonardo ◽  
2006 ◽  
Vol 39 (3) ◽  
pp. 233-235 ◽  
Author(s):  
Eric D. Demaine ◽  
Martin L. Demaine ◽  
A. Laurie Palmer
Keyword(s):  
Three Dimensional ◽  
Dimensional Chain ◽  
Mathematical Basis ◽  
One Dimensional ◽  
Dimensional Shape ◽  
Folding Structure ◽  
Three Dimensional Shape

The Helium Stockpile is a manipulable folding structure of hundreds of wooden blocks, representing the transformation between surface and solid through a foldable one-dimensional chain. The sculpture grew out of an unexpected collaboration between a sculptor and two mathematicians, giving the structure a mathematical basis through which it is guaranteed to be foldable into essentially any three-dimensional shape.

scholarly journals Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images

2020 ◽  
Vol 2 (4) ◽  
pp. 175-186
Author(s):  
Dr. Samuel Manoharan ◽  
Sathish
Keyword(s):  
Detection System ◽  
Automatic Segmentation ◽  
Pulmonary Nodules ◽  
Automated Image Analysis ◽  
Computer Aided Detection ◽  
Response Characteristics ◽  
Proposed Model ◽  
Neural Classifier ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Computer aided detection system was developed to identify the pulmonary nodules to diagnose the cancer cells. Main aim of this research enables an automated image analysis and malignancy calculation through data and CPU infrastructure. Our proposed algorithm has improvement filter to enhance the imported images and for nodule selection and neural classifier for false reduction. The proposed model is experimented in both internal and external nodules and the obtained results are shown as response characteristics curves.

scholarly journals Texture feature based automated seeded region growing in abdominal MRI segmentation

2009 ◽  
Vol 02 (01) ◽  
pp. 1-8 ◽  
Author(s):  
Jie Wu ◽  
Skip Poehlman ◽  
Michael D. Noseworthy ◽  
Markad V. Kamath
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Mri Segmentation ◽  
Seeded Region Growing ◽  
Abdominal Mri ◽  
Feature Based

scholarly journals La transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grises

2018 ◽  
Vol 17 (32) ◽  
pp. 213-227
Author(s):  
Ricardo Joaquín de Armas Costa ◽  
Shirley Viviana Quintero Torres ◽  
Cristina Acosta Muñoz ◽  
Carlos Camilo Guillermo Rey Torres
Keyword(s):  
Region Growing ◽  
Seeded Region Growing

En este artículo de investigación científica se da a conocer a la comunidad interesada en el procesamiento digital de imágenes, una aplicación inédita de la transformada de Radon para segmentar imágenes en escala de grises, lo que permite la identificación y clasificación de regiones u objetos, misma que puede extenderse a imágenes en color. Los resultados obtenidos se compararon con los resultados de dos algoritmos clásicos de segmentación: el algoritmo de umbralización Otsu optimizado, y el algoritmo de crecimiento de regiones Seeded Region Growing.

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