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A cad scheme for early lung cancer detection in chest radiography
Abstract
The purpose of this work is to describe a chest radiography computer-Aided diagnostic (CAD) scheme designed to analyze the chest radiographs performed in the framework of the Galician (Spain) Health Service (GHS), including the radiographs that are not reported by the radiologists. The final goal of this CAD system is its integration in the GHS daily clinical environment, with a feasible RIS-PACS-CAD and EHR-CAD integration model. The database of the study included 55 chest radiographies with 64 nodules/lung cancer. This database was used to develop and test the CAD system in our research laboratory. Free-Response Receiver Operating Characteristic (FROC) curves were employed to evaluate the performance of the CAD system. An independent database was employed to evaluate the performance of the CAD system by external radiologists. After the application of a linear classifier, our CAD system achieved a sensitivity of 70% with a false positive rate between 4 and 6 per image depending on the testing database. When compared with other commercial systems, our CAD scheme achieved similar performance results. Therefore, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in chest radiography, and therefore, it can be integrated in the clinical practice.
... Over the past few decades, several CAD systems have been approved by the US Food and Drug Administration to help in imaging situations [13]. Many investigators have studied CAD for diagnosis of various types of diseases, including lung diseases [14,15], breast cancer [16,17], stroke [18], liver cancer [19], microcalcifications [20], and artery disease [21]. ...
Purpose:
The aim of this paper is to describe a project designed to achieve a total integration of different CAD algorithms into the PACS environment by using a wide computing infrastructure.
Methods:
The aim is to build a system for the entire region of Galicia, Spain, to make CAD accessible to multiple hospitals by employing different PACSs and clinical workstations. The new CAD model seeks to connect different devices (CAD systems, acquisition modalities, workstations and PACS) by means of networking based on a platform that will offer different CAD services. This paper describes some aspects related to the health services of the region where the project was developed, CAD algorithms that were either employed or selected for inclusion in the project, and several technical aspects and results.
Results:
We have built a standard-based platform with which users can request a CAD service and receive the results in their local PACS. The process runs through a web interface that allows sending data to the different CAD services. A DICOM SR object is received with the results of the algorithms stored inside the original study in the proper folder with the original images.
Conclusions:
As a result, a homogeneous service to the different hospitals of the region will be offered. End users will benefit from a homogeneous workflow and a standardised integration model to request and obtain results from CAD systems in any modality, not dependant on commercial integration models. This new solution will foster the deployment of these technologies in the entire region of Galicia.
We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.