A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

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AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

Image Analysis & Stereology ◽

10.5566/ias.1679 ◽

2017 ◽

Vol 36 (2) ◽

pp. 65 ◽

Cited By ~ 7

Author(s):

Elaheh Aghabalaei Khordehchi ◽

Ahmad Ayatollahi ◽

Mohammad Reza Daliri

Keyword(s):

Lung Nodule ◽

Three Dimensional ◽

Pulmonary Nodules ◽

Ct Images ◽

Support Vector ◽

Svm Classifier ◽

Region Merging ◽

Three Dimensional Objects ◽

Ct Data ◽

Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

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Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

Frontiers in Public Health ◽

10.3389/fpubh.2021.671070 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinglun Liang ◽

Guoliang Ye ◽

Jianwen Guo ◽

Qifan Huang ◽

Shaohui Zhang

Keyword(s):

Lung Cancer ◽

Early Stage ◽

Pulmonary Nodules ◽

Ct Images ◽

False Positives ◽

Classification Systems ◽

Cad System ◽

Nodule Detection ◽

Diagnosis Of Lung Cancer ◽

Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

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Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

10.1117/12.911782 ◽

2012 ◽

Cited By ~ 3

Author(s):

Bin Chen ◽

Takayuki Kitasaka ◽

Hirotoshi Honma ◽

Hirotsugu Takabatake ◽

Masaki Mori ◽

...

Keyword(s):

Structure Analysis ◽

Automatic Segmentation ◽

Pulmonary Nodules ◽

Ct Images ◽

Chest Ct ◽

Solitary Pulmonary Nodules ◽

Local Intensity

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3D texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images

10.1117/12.2007016 ◽

2013 ◽

Cited By ~ 7

Author(s):

Ashis Kumar Dhara ◽

Sudipta Mukhopadhyay ◽

Niranjan Khandelwal

Keyword(s):

Texture Analysis ◽

Pulmonary Nodules ◽

Ct Images ◽

Solitary Pulmonary Nodules ◽

3D Texture ◽

3D Texture Analysis ◽

Occurrence Matrix ◽

Lung Ct

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Non-surgical management of early-stage lung cancer

10.1093/med/9780199657742.003.0004 ◽

2017 ◽

Author(s):

Jim Brown ◽

Neal Navani

Keyword(s):

Lung Cancer ◽

Early Stage ◽

Pulmonary Nodules ◽

The Elderly ◽

Multidisciplinary Teams ◽

Solitary Pulmonary Nodules ◽

Early Stage Lung Cancer ◽

Mortality And Morbidity ◽

Inoperable Patient ◽

Ablative Techniques

As low-dose computed tomography screening of ‘high-risk’ smokers is occurring with increasing frequency, the incidental discovery of solitary pulmonary nodules is becoming more frequent, and lung cancer multidisciplinary teams are now often faced with balancing risk and benefit when making decisions regarding the radical treatment of patients with a clinical diagnosis of early lung cancer but borderline fitness. Surgery offers the best prospect of cure but is associated with significant mortality and morbidity; the elderly and frail experience more toxicity and a greater impact on the quality of life. This chapter reviews the criteria for assessing surgical fitness and examines the evidence for minimally invasive and ablative techniques for the treatment of early peripheral lung cancer in the medically inoperable patient.

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Diagnosis and outcome of resected solitary pulmonary nodules after liver transplantation

Japanese Journal of Clinical Oncology ◽

10.1093/jjco/hyz159 ◽

2019 ◽

Author(s):

Mari Tone ◽

Nobuyasu Awano ◽

Takehiro Izumo ◽

Hanako Yoshimura ◽

Tatsunori Jo ◽

...

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Liver Transplantation ◽

Early Stage ◽

Lung Resection ◽

Invasive Pulmonary Aspergillosis ◽

Primary Lung Cancer ◽

Pulmonary Nodules ◽

Solitary Pulmonary Nodules ◽

Computed Tomography Scans

Abstract Objective Solitary pulmonary nodules after liver transplantation are challenging clinical problems. Herein, we report the causes and clinical courses of resected solitary pulmonary nodules in patients who underwent liver transplantation. Methods We retrospectively obtained medical records of 68 patients who underwent liver transplantation between March 2009 and June 2016. This study mainly focused on patients with solitary pulmonary nodules observed on computed tomography scans during follow-ups that were conducted until their deaths or February 2019. Results Computed tomography scans revealed solitary pulmonary nodules in 7 of the 68 patients. Definitive diagnoses were obtained using video-assisted lung resection in all seven patients. None experienced major postoperative complications. The final pathologic diagnoses were primary lung cancer in three patients, pulmonary metastases from hepatocellular carcinoma in one patient, invasive pulmonary aspergillosis in one patient, post-transplant lymphoproliferative disorder in one patient, and hemorrhagic infarction in one patient. The three patients with lung cancer were subsequently treated with standard curative resection. Conclusions Solitary pulmonary nodules present in several serious but potentially curable diseases, such as early-stage lung cancer. Patients who present with solitary pulmonary nodules after liver transplantation should be evaluated by standard diagnostic procedures, including surgical biopsy if necessary.

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