1
|
Mridha MF, Prodeep AR, Hoque ASMM, Islam MR, Lima AA, Kabir MM, Hamid MA, Watanobe Y. A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
Collapse
Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, American International University Bangladesh, Dhaka 1229, Bangladesh
| | - Akibur Rahman Prodeep
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - A. S. M. Morshedul Hoque
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Abdul Hamid
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
| |
Collapse
|
2
|
An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6727957. [PMID: 36212245 PMCID: PMC9537033 DOI: 10.1155/2022/6727957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/02/2021] [Accepted: 01/06/2022] [Indexed: 11/24/2022]
Abstract
Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.
Collapse
|
3
|
Moragheb MA, Badie A, Noshad A. An Effective Approach for Automated Lung Node Detection using CT Scans. J Biomed Phys Eng 2022; 12:377-386. [PMID: 36059280 PMCID: PMC9395629 DOI: 10.31661/jbpe.v0i0.2110-1412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.
Collapse
Affiliation(s)
- Mohammad Amin Moragheb
- MSc, Department of Computer Engineering, Faculty of Engineering, Mamasani Higher Education Center, Mamasani, Iran
| | - Ali Badie
- MSc, Department of Computer Engineering, Faculty of Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
| | - Ali Noshad
- BSc, Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
| |
Collapse
|
4
|
Tyagi S, Talbar SN. CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation. Comput Biol Med 2022; 147:105781. [DOI: 10.1016/j.compbiomed.2022.105781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 06/16/2022] [Accepted: 06/19/2022] [Indexed: 11/03/2022]
|
5
|
Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5112867. [PMID: 35371290 PMCID: PMC8967527 DOI: 10.1155/2022/5112867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/21/2022] [Accepted: 03/09/2022] [Indexed: 11/24/2022]
Abstract
Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors' work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors' diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor's diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.
Collapse
|
6
|
Shariaty F, Orooji M, Velichko EN, Zavjalov SV. Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest. Comput Biol Med 2022; 140:105086. [PMID: 34861641 DOI: 10.1016/j.compbiomed.2021.105086] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/03/2022]
Abstract
Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging. In general, the process line of a CAD system can be divided into four main stages: preprocessing, localization, feature extraction, and classification. As segmentation is required for localization in computer vision and medical image analysis, this step has become a major and challenging problem, and much research has been done on new segmentation techniques. In recent years, interest in model-based segmentation methods has increased, and the reason for this is even if some object information is lost, such gaps can be filled by using the previous information in the model. This paper proposed Texture Appearance Model (TAM), which is a new model-based method and segments all types of nodule areas accurately and efficiently, including juxta-pleural nodules, without separating the lung from the surrounding area in a CT scan of the lung. In this method, Texture Representation of Image (TRI) is obtained using tissue texture feature extraction and feature selection algorithms. The proposed method has been evaluated in 85 nodules of the dataset, received from the Iranian hospital, in which the ground-truth annotation by physicians and CT imaging data were provided. The results show that the proposed algorithm has an encouraging performance for distinguishing different types of nodules, including pleural, cavity and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 84.75%.
Collapse
Affiliation(s)
- Faridoddin Shariaty
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia.
| | - Mahdi Orooji
- Department of Electrical and Computer Engineering, University of California, Davis, United States
| | - Elena N Velichko
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
| | - Sergey V Zavjalov
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
| |
Collapse
|
7
|
Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J Digit Imaging 2021; 33:655-677. [PMID: 31997045 DOI: 10.1007/s10278-020-00320-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
Collapse
|
8
|
|
9
|
Abstract
Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.
Collapse
|
10
|
Fu B, Wang G, Wu M, Li W, Zheng Y, Chu Z, Lv F. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study. Eur J Radiol 2020; 126:108928. [DOI: 10.1016/j.ejrad.2020.108928] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 01/05/2020] [Accepted: 02/28/2020] [Indexed: 12/29/2022]
|
11
|
3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review. J Digit Imaging 2019; 31:799-850. [PMID: 29915942 DOI: 10.1007/s10278-018-0101-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
Collapse
|
12
|
Le V, Yang D, Zhu Y, Zheng B, Bai C, Shi H, Hu J, Zhai C, Lu S. Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 160:141-151. [PMID: 29728241 DOI: 10.1016/j.cmpb.2018.04.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 02/21/2018] [Accepted: 04/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES To improve lung nodule classification efficiency, we propose a lung nodule CT image characterization method. We propose a multi-directional feature extraction method to effectively represent nodules of different risk levels. The proposed feature combined with pattern recognition model to classify lung adenocarcinomas risk to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). METHODS First, we constructed the reference map using an integral image and labelled this map using a K-means approach. The density distribution map of the lung nodule image was generated after scanning all pixels in the nodule image. An exponential function was designed to weight the angular histogram for each component of the distribution map, and the features of the image were described. Then, quantitative measurement was performed using a Random Forest classifier. The evaluation data were obtained from the LIDC-IDRI database and the CT database which provided by Shanghai Zhongshan hospital (ZSDB). In the LIDC-IDRI, the nodules are categorized into three configurations with five ranks of malignancy ("1" to "5"). In the ZSDB, the nodule categories are AAH, AIS, MIA, and IA. RESULTS The average of Student's t-test p-values were less than 0.02. The AUCs for the LIDC-IDRI database were 0.9568, 0.9320, and 0.8288 for Configurations 1, 2, and 3, respectively. The AUCs for the ZSDB were 0.9771, 0.9917, 0.9590, and 0.9971 for AAH, AIS, MIA and IA, respectively. CONCLUSION The experimental results demonstrate that the proposed method outperforms the state-of-the-art and is robust for different lung CT image datasets.
Collapse
Affiliation(s)
- Vanbang Le
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, Postcode 200237, China
| | - Dawei Yang
- Department of Pulmonary Medicine, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China; Shanghai Respiratory Research Institute, Shanghai, Postcode 200032, China
| | - Yu Zhu
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, Postcode 200237, China.
| | - Bingbing Zheng
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, Postcode 200237, China.
| | - Chunxue Bai
- Department of Pulmonary Medicine, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China; Shanghai Respiratory Research Institute, Shanghai, Postcode 200032, China.
| | - Hongcheng Shi
- Department of Nuclear Medicine, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China.
| | - Jie Hu
- Department of Pulmonary Medicine, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China; Shanghai Respiratory Research Institute, Shanghai, Postcode 200032, China
| | - Changwen Zhai
- Department of Pathology, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China.
| | - Shaohua Lu
- Department of Pathology, ZhongShan Hospital, Fudan University, Shanghai, Postcode 200032, China
| |
Collapse
|
13
|
3-D segmentation of lung nodules using hybrid level sets. Comput Biol Med 2018; 96:214-226. [PMID: 29631230 DOI: 10.1016/j.compbiomed.2018.03.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/22/2018] [Accepted: 03/22/2018] [Indexed: 11/20/2022]
Abstract
Lung nodule segmentation in CT images and its subsequent volume analysis can help determine the malignancy status of a lung nodule. While several efficient segmentation schemes have been proposed, only a few studies evaluated the segmentation's performance for large nodules. In this research, we contribute a semi-automatic system which is capable of performing robust 3-D segmentations on both small and large nodules with good accuracy. The target CT volume is de-noised with an anisotropic diffusion filter and a region of interest is selected around the target nodule on a reference slice. The proposed model performs nodule segmentation by incorporating a mean intensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptive technique using image intensity histogram to estimate the desired mean intensity of the nodule. The proposed system is validated on both lung nodules and phantoms collected from publicly available diverse databases. Quantitative and visual comparative analysis of the proposed work with the Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented. The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, the mean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved results suggest that the proposed method can be used for volume estimations of small as well as large-sized nodules.
Collapse
|
14
|
Zia ur Rehman M, Javaid M, Shah SIA, Gilani SO, Jamil M, Butt SI. An appraisal of nodules detection techniques for lung cancer in CT images. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
15
|
Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT. Med Phys 2017; 45:297-306. [DOI: 10.1002/mp.12690] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 08/24/2017] [Accepted: 10/26/2017] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jungwon Cha
- Medical Imaging Laboratory; University of Louisville; Louisville KY 40292 USA
| | | | - Neal Dunlap
- Department of Radiation Oncology; Brown Cancer Center; University of Louisville; Louisville KY 40202 USA
| | - Amir A. Amini
- Medical Imaging Laboratory; University of Louisville; Louisville KY 40292 USA
| |
Collapse
|
16
|
Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0317-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
17
|
Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS. Automatic 3D pulmonary nodule detection in CT images: A survey. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:91-107. [PMID: 26652979 DOI: 10.1016/j.cmpb.2015.10.006] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 09/01/2015] [Accepted: 10/03/2015] [Indexed: 06/05/2023]
Abstract
This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.
Collapse
Affiliation(s)
- Igor Rafael S Valente
- Instituto Federal do Ceará, Campus Maracanaú, Av. Parque Central, S/N, Distrito Industrial I, 61939-140 Maracanaú, Ceará, Brazil; Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Paulo César Cortez
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Edson Cavalcanti Neto
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - José Marques Soares
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Victor Hugo C de Albuquerque
- Programa de Pós-Graduacão em Informática Aplicada, Universidade de Fortaleza, Av. Washington Soares, 1321, Edson Queiroz, 60811341, CEP 608113-41 Fortaleza, Ceará, Brazil
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovacão em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, S/N, 4200-465 Porto, Portugal.
| |
Collapse
|
18
|
|
19
|
Watershed based intelligent scissors. Comput Med Imaging Graph 2015; 43:122-9. [DOI: 10.1016/j.compmedimag.2015.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Revised: 11/26/2014] [Accepted: 01/09/2015] [Indexed: 11/17/2022]
|
20
|
Guo X, Huang S, Fu X, Wang B, Huang X. Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method. Biomed Eng Online 2015; 14:57. [PMID: 26087652 PMCID: PMC4472182 DOI: 10.1186/s12938-015-0055-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 06/09/2015] [Indexed: 11/14/2022] Open
Abstract
Background Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. Methods In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. Results Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. Conclusions An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.
Collapse
Affiliation(s)
- Xiaoxi Guo
- Computer Science Department, Xiamen University, Xiamen, China. .,Computer Engineering College, Jimei University, Xiamen, China.
| | - Shaohui Huang
- Computer Science Department, Xiamen University, Xiamen, China.
| | - Xiaozhu Fu
- Computer Science Department, Xiamen University, Xiamen, China.
| | - Boliang Wang
- Computer Science Department, Xiamen University, Xiamen, China.
| | - Xiaoyang Huang
- Computer Science Department, Xiamen University, Xiamen, China.
| |
Collapse
|
21
|
Features extraction in anterior and posterior cruciate ligaments analysis. Comput Med Imaging Graph 2015; 46 Pt 2:108-20. [PMID: 25825212 DOI: 10.1016/j.compmedimag.2015.03.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 02/09/2015] [Accepted: 03/02/2015] [Indexed: 01/11/2023]
Abstract
The main aim of this research is finding the feature vectors of the anterior and posterior cruciate ligaments (ACL and PCL). These feature vectors have to clearly define the ligaments structure and make it easier to diagnose them. Extraction of feature vectors is obtained by analysis of both anterior and posterior cruciate ligaments. This procedure is performed after the extraction process of both ligaments. In the first stage in order to reduce the area of analysis a region of interest including cruciate ligaments (CL) is outlined in order to reduce the area of analysis. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges has been implemented. After finding the region of interest (ROI), the fuzzy connectedness procedure is performed. This procedure permits to extract the anterior and posterior cruciate ligament structures. In the last stage, on the basis of the extracted anterior and posterior cruciate ligament structures, 3-dimensional models of the anterior and posterior cruciate ligament are built and the feature vectors created. This methodology has been implemented in MATLAB and tested on clinical T1-weighted magnetic resonance imaging (MRI) slices of the knee joint. The 3D display is based on the Visualization Toolkit (VTK).
Collapse
|