Rodolfo Acatauassú Nunes

Methodology for automatic detection of lung nodules in computerized tomography images

Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high. Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages. The automated lung nodules detection scheme consists of six stages: thorax extraction, lung extraction, lung reconstruction, structures extraction, tubular structures elimination, and false positive reduction. In the thorax extraction stage all the artifacts external to the patients body are discarded. Lung extraction stage is responsible for the identification of the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect elimination of portions belonging to the parenchyma. Structures extraction stage comprises the selection of dense structures from inside the lung parenchyma. The next stage, tubular structures elimination eliminates a great part of the pulmonary trees. Finally, the false positive stage selects only structures with great probability to be nodule. Each of the several stages has very specific objectives in detection of particular cases of lung nodules, ensuring good matching rates even in difficult detection situations. We use 33 exams with diversified diagnosis and slices numbers for validating the methodology. We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The total classification sensitivity obtained, measured out of the nodule candidates, was 84.84%. The specificity achieved was 96.15% and the total accuracy of the method was 95.21%.

Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

OBJECTIVE The present work has the objective of developing an automatic methodology for the detection of lung nodules. METHODOLOGY The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures. In the second stage, nodule candidates are segmented. Finally, in the third stage, shape and texture features are extracted, selected and then classified using a support vector machine. RESULTS In the testing stage, with 140 new exams from the Lung Image Database Consortium image collection, 80% of which are for training and 20% are for testing, good results were achieved, as indicated by a sensitivity of 85.91%, a specificity of 97.70% and an accuracy of 97.55%, with a false positive rate of 1.82 per exam and 0.008 per slice and an area under the free response operating characteristic of 0.8062. CONCLUSION Lung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis considerably increases the survival chance of patients. The methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.

Automatic segmentation of lung nodules with growing neural gas and support vector machine

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures. This work seeks to develop a methodology for automatic detection of lung nodules. The proposed method consists of the acquisition of computerized tomography images of the lung, the reduction of the volume of interest through techniques for the extraction of the thorax, extraction of the lung, and reconstruction of the original shape of the parenchyma. After that, growing neural gas (GNG) is applied to constrain even more the structures that are denser than the pulmonary parenchyma (nodules, blood vessels, bronchi, etc.). The next stage is the separation of the structures resembling lung nodules from other structures, such as vessels and bronchi. Finally, the structures are classified as either nodule or non-nodule, through shape and texture measurements together with support vector machine. The methodology ensures that nodules of reasonable size be found with 86% sensitivity and 91% specificity. This results in a mean accuracy of 91% for 10 experiments of training and testing in a sample of 48 nodules occurring in 29 exams. The rate of false positives per exam was of 0.138, for the 29 exams analyzed.

Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Lung cancer stands out among all other types of cancer for presenting one of the highest incidence rates and one of the highest rates of mortality. Unfortunately, this disease is often diagnosed late, affecting the treatment result. One of the hopes for changing this scenario lies in achieving a more precocious diagnosis of lung cancer through low-dose computed tomography, used as a screening method in risk groups of smokers or former smokers with elevated tobacco load. In order to help specialists in this search and identification of lung nodules in tomographic images, many research centers develop computer-aided detection systems (CAD systems) which are intended to automate procedures. This work has the purpose of developing a methodology for automatic detection of small lung nodules (with sizes between 2 and 10mm) through image processing and pattern recognition techniques. Some of these techniques are widely used in similar applications, as is the case of the region growing technique for segmentation of the pulmonary parenchyma. Other techniques, with more restricted application, are the Gaussian mixture models and the Hessian matrix for segmentation of structures inside the lung, Tsalliss and Shannons entropy measurements as texture descriptors, and support vector machine to classify suspect regions as either nodules or non-nodules. The results achieved with the use of this set of techniques, applied to a sample with 28 exams from a public database, showed that small nodules were detected with a sensitivity of 90.6%, a specificity of 85% and an accuracy of 88.4%. The rate of false positives per exam was of 1.17. Graphical abstractDisplay Omitted HighlightsWe present a methodology for automatic detection of small lung nodules.Gaussian Mixture Models are used to segment regions that are likely to be nodules.False positive reduction with SVM and entropy measures of Tsallis and Shannon.Tests use a sample of 72 nodules occurring in 28 exams from LIDC image database.Presents sensitivity of 90.6%, specificity of 85%, accuracy of 88.4%, and 1.17 FP/i.

The assessment of telemedicine to support outpatient palliative care in advanced cancer.

OBJECTIVE We aimed to examine telemedicine as a form of home and additional support for traditional outpatient care as a way to remotely monitor and manage the symptoms of patients with advanced cancer. METHOD In total, 12 patients were monitored through monthly consultations with a multidisciplinary healthcare team and weekly web conferences. To evaluate and treat pain and other symptoms, the Edmonton Symptom Assessment System (ESAS) was applied during all remote or in-person interviews. RESULTS During monitoring, the team contacted the patients on 305 occasions: there were 89 consultations at the hospital, 19 in-person assistances to the family (without the patient), 77 web conferences, 38 telephone calls, 80 emails, and 2 home visits. The mean monitoring time until death was 195 ± 175.1 days. Eight patients who completed the ESAS in all interviews had lower mean distress symptom scores according to web conferences than in person. SIGNIFICANCE OF RESULTS Telemedicine allowed greater access to the healthcare system, reduced the need to employ emergency services, improved assessment/control of symptoms, and provided greater orientation and confidence in the care given by family members through early and proactive interventions. Web conferencing proved to be a good adjuvant to home monitoring of symptoms, complementing in-person assistance.

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.

Biópsia aspirativa transtorácica por agulha fina guiada por TC de lesões pulmonares: resultados e complicações

OBJECTIVE To analyze the cytological findings of CT-guided percutaneous fine-needle aspiration biopsies of the lung, to demonstrate the diagnostic feasibility of the method in the investigation of pulmonary lesions, and to determine the complications of the procedure, evaluating its safety. METHODS A retrospective analysis of 89 patients with various types of pulmonary lesions who underwent 97 procedures over a period of five years. The patients were divided into groups regarding the indication for the procedure: suspicion of primary lung cancer (stages IIIB or IV); suspicion of lung cancer (stages I, II, or IIIA) and clinical contraindications for surgery; suspicion of pulmonary metastasis from other organs; and pulmonary lesions with benign radiological aspect. All of the procedures were performed with 25-gauge needles and were guided by spiral CT. The final diagnosis was confirmed by surgical biopsy and clinical/oncological follow-up. For the analysis of complications, the total number of procedures was considered. RESULTS The main indication for the procedure was suspicion of advanced-stage primary lung cancer. The accuracy of the method for malignant lesions was 91.5%. The lesion was confirmed as cancer in 73% of the patients. The major complication was pneumothorax (27.8%), which required chest tube drainage in 12.4% of the procedures. CONCLUSIONS The principal indication for CT-guided fine-needle biopsy was suspicion of primary lung cancer in patients who were not surgical candidates. The procedure has high diagnostic feasibility for malignant pulmonary diseases. The most prevalent complication was pneumothorax. However, in most cases, chest tube drainage was unnecessary. No deaths were related to the procedure.

CPAP increases 6-minute walk distance after lung resection surgery.

BACKGROUND: The application of CPAP has been used to minimize postoperative pulmonary complications after lung resection surgery. The aim of this study was to quantify both the CPAP effects upon lung function and functional capacity in early postoperative lung resection, as well as to evaluate if CPAP prolongs air leak through the chest drain. METHODS: Thirty patients in the postoperative period of lung resection were allocated into 2 groups: an experimental group, consisting of 15 patients who underwent a 10 cm H2O CPAP, and a 15 patient control group, who performed breathing exercises. Arterial blood gas analysis, peak expiratory flow (PEF), respiratory muscle strength, spirometry, and 6-min walk test (6MWT) were assessed in the preoperative period, and repeated postoperatively on the first and on the seventh day (6MWT was repeated only on the seventh day). RESULTS: Significant increases in PEF, muscle strength, and FEV1 between the first and seventh postoperative day were observed, both in the experimental and in the control group, whereas FVC and PaO2 increased significantly between the first and seventh postoperative day only in the experimental group. The average loss in 6-min walk distance (6MWD) from preoperative to postoperative day 7 in the experimental group was significantly lower than in control group. When comparing the 2 groups, only 6MWD was statistically different (P < .001). There was no air leakage increase through the drain with the early use of CPAP. CONCLUSION: When compared to breathing exercises, CPAP increases the 6MWD in postoperative lung resection patients, without prolonging air leak through the chest drain.

Diagnosis of solitary lung nodules using the local form of Ripley's K function applied to three-dimensional CT data

This paper analyzes the application of Ripleys K function to characterize lung nodules as malignant or benign in computerized tomography images. The proposed characterization method is based on a selection of measures from Ripleys K function to discriminate between benign and malignant nodules, using stepwise discriminant analysis. Based on the selected measures, a linear discriminant analysis procedure is performed once again in order to predict the classification of each nodule. To evaluate the ability of these features to discriminate the nodules, a set of tests was carried out using a sample of 39 pulmonary nodules, 29 benign and 10 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminators performance. The best setting of the analyzed function in the tested sample presented 70.0% of sensitivity but with 100.0% of specificity and 92.3% of accuracy. Thus, preliminary results of this approach are very promising regarding its contribution to the diagnosis of pulmonary nodules, but it still needs to be tested with larger series and associated to other quantitative imaging methods in order to improve global performance.

Lung nodule classification using artificial crawlers, directional texture and support vector machine

Abstract Lung cancer is the major cause of death among patients with cancer throughout the world. The main symptom that indicate the lung cancer is the presence of lung nodules. This work proposes a methodology to classify lung nodule and non-nodule using texture features. The state-of-art of the presented work are the adaption of the Artificial Crawlers and Rose Diagram techniques for representing patterns over 3D images. Several information are extracted based on the texture behavior of these methods, allowing the correct classification of lung nodules candidates using Support Vector. Objective: This work proposes a methodology to classify lung nodule candidates and non-nodule candidates based on computed tomography (CT) images. Methodology: The Lung Image Database Consortium (LIDC-IDRI) image database is employed for our tests. Three techniques are employed to extract texture measurements. The first technique is artificial crawlers (ACs), an artificial life algorithm. The second technique uses the rose diagram (RD) to extract directional measurements. The third technique is a hybrid model that combines texture measurements from artificial crawlers and the rose diagram. The support vector machine (SVM) classifier with a radial basis kernel is employed. Results: In the testing stage, we used 833 scans from the LIDC-IDRI database. For the application of the methodology, we decided to divide the whole database into two groups, training and testing. We used partitions of training and testing of 20/80%, 40/60%, 60/40% and 80/20%. The division was repeated 5 times at random. We reached a mean accuracy (mACC) of 94.30%, a mean sensitivity (mSEN) of 91.86%, a mean specificity (mSPC) of 94.78%, a coefficient of accuracy variance (CAv) of 1.61% and a mean area under the receiver operating characteristic (mROC) curves of 0.922. Conclusion: Lung cancer has the highest mortality rate and one of the smallest survival rates after diagnosis. An early diagnosis increases the survival chance of patients. The proposed methodology is a useful tool for specialists in the detection of nodules. We believe we contribute for the expert system field because 1) the adaption of the Artificial Crawlers and Rose Diagram methods as 3D texture descriptors is innovative and contains great potential; 2) we adapted and developed measurements from the 3D texture descriptors; and 3) the simplicity and discriminative power of the methodology can be extended to applications based on images with other contexts.