Computational method for aid in the diagnosis of sixth optic nerve palsy through digital videos.

The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela. The usual methods for diagnosing the sixth nerve palsy are invasive or depend on expensive equipment, and computer-based methods designed specifically to diagnose the aforementioned palsy were not found until the publication of this work. Therefore, a low-cost, non-invasive method can support or guide the ophthalmologist's diagnosis. In this context, this work presents a computational methodology to aid in diagnosing the sixth nerve palsy using videos to assist ophthalmologists in the diagnostic process, serving as a second opinion. The proposed method uses convolutional neural networks and image processing techniques to track both eyes' movement trajectory during the video. With this trajectory, it is possible to calculate the average velocity (AV) in which each eye moves. Since it is known that paretic eyes move slower than healthy eyes, comparing the AV of both eyes can determine if the eye is healthy or paretic. The results obtained with the proposed method showed that paretic eyes move at least 19.65% slower than healthy ones. This threshold, along with the AV of the movement of the eyes, can help ophthalmologists in their analysis. The proposed method reached 92.64% accuracy in diagnosing the sixth optic nerve palsy (SONP), with a Kappa index of 0.925, which highlights the reliability of the results and gives favorable perspectives for further clinical application.

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost.

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

Surgical planning of horizontal strabismus using multiple output regression tree.

Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems. The most severe cases require surgical intervention, despite other less invasive techniques being available for a more conservative approach. As for surgeries, the treatment goal is to align the eyes to recover binocular vision, which demands knowledge, training, and experience. One of the leading causes of failure is human error during the measurement of deviation. Thus, this work proposes a new method based on the Decision Tree Regressor algorithms to assist in the surgical planning for horizontal strabismus to predict recoil and resection measures in the lateral and medial rectus muscles. In the presented method, two application approaches were taken, being in the form of multiple single target models, one procedure at a time, and the form of one multiple target model or all surgical procedures together. The method's efficiency is indicated by the average difference between the value indicated by the method and the physician's value. In our most accurate model, an average error of 0.66 mm was obtained for all surgical procedures, both for resection and recoil in the indication of the horizontal strabismus surgical planning. The results present the feasibility of using Decision Tree Regressor algorithms to perform the planning of strabismus surgeries, making it possible to predict correction values for surgical procedures based on medical data analysis and exceeding state-of-art.

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram.

BACKGROUND: The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures. OBJECTIVES: The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images. METHODS: A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus. RESULTS: The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively. CONCLUSIONS: KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.

Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance.

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees. Graphical Abstract Stages of the proposed methodology.

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM.

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

3D shape analysis to reduce false positives for lung nodule detection systems.

Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

Voxel-based comparative analysis of lung lesions in CT for therapeutic purposes.

Lung cancer remains as one of the most incident types of cancer throughout the world. Temporal evaluation has become a very useful tool when one wishes to analyze some malignancy-indicating behavior. The objective of the present work is to detect changes in the local densities of lung lesions over time (follow-up analysis). From the detected changes, local information as well as extent region of changes can complement the studies regarding the malignant or benign nature of the lesion. Based on this idea, we attempt to use techniques that allow the observation of changes in the lesion over time, based on remote sensing techniques which highlight changes occurring in the environment. The techniques used were the image differencing, image rationing, median filtering, image regression and the fuzzy XOR operator. Based on the global measurement of change percentage in the density, we found density variations which were considered significant in a range from 2.22 to 36.57 % of the volume of the lesion. The results achieved are promising since, besides the visual aspects of the changes in density of the lung lesion over time, we managed to quantify these changes and compare them by volumetric analysis, a more commonly used technique for analysis of changes in lung lesions.

Taxonomic indexes for differentiating malignancy of lung nodules on CT images

Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.

Predição volumétrica de lesões pulmonares aplicando modelo oculto de Markov / Prediction volumetric of lung lesions applying hidden Markov model

OBJETIVO: predizer o estado volumétrico de lesões pulmonares aplicando o modelo oculto de Markov (HMM). MATERIAIS E MÉTODOS: Aquisição de imagens de lesões pulmonares temporais, geração do HMM e a aplicação do HMM. RESULTADOS: Os testes foram aplicados em 24 lesões pulmonares, adquiridas da Public Lung Database to Address Drug Response (PLDADR). Dividimos os resultados desta pesquisa em 3. O primeiro utilizando a base completa para predição volumétrica da lesão e comparação com o Response Evaluation Criteria in Solid Tumors (RECIST), atingindo uma taxa de acerto de 70,83%. No segundo, Aplica - se o método leave-one-out, separando os dados em dois grupos, treino e teste, obtendo-se uma taxa de acerto de 75,00%. Por fim, realizamos a predição volumétrica de cada lesão no intervalo de 5 tempos. O resultado mostrou que é possível predizer se o estado da lesão está progredindo, regredindo ou estabilizando, a partir das alterações ocorridas nos diâmetros e volumes.

OBJECTIVE: predicting the volume status of lung lesions by applying the hidden Markov model (HMM). MATERIALS AND METHODS: Acquisition of images of temporal lung lesions, HMM generation and application of HMM. RESULTS: The tests were applied in 24 pulmonary lesions, acquired from Public Lung Database to Address Drug Response(PLDADR). We have divided this search in 3. The first using the full volumetric basis for prediction of the lesion and compared to the Response Evaluation Criteria in Solid Tumors (RECIST), reaching a 70.83% success rate. Then, weapply the leave-one-out method, separating the data into two groups, training and testing, yielding a 75.00% successrate. Finally, we volumetric prediction of each lesion in 5 days interval. The result showed that it is possible to predict the state of the injury is progressing, regressing or stabilizing, from changes in the diameters and volumes.

Diagnóstico de glaucoma em retinografias baseado em geoestatística / Glaucoma diagnosis in retinography based on geostatistics

O glaucoma é uma das doenças que mais causam cegueira em todo o mundo. O Conselho Brasileiro de Oftalmologia (CBO) estima que no Brasil existam 985 mil portadores de glaucoma com mais de 40 anos de idade. A utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de detecção e diagnósticos corretos,auxiliando os especialistas na tomada de decisões sobre o tratamento do glaucoma. OBJETIVO: Apresentar um método para diagnóstico do glaucoma em retinografias utilizando o LBP para representar a região do disco ótico, funções geoestatísticas para descrever padrões e o MVS para classificar as imagens. MÉTODOS: Executado em 3 etapas: Representação da imagem (1), Extração de Características com geoestatística (2) e Classificação e Validação (3). RESULTADOS: Foram obtidos 88% de especificidade, 82% de sensibilidade e 84% de acurácia no diagnóstico do glaucoma. CONCLUSÃO: O método mostrou-se promissor como uma forma de auxílio ao diagnóstico de glaucoma.

Glaucoma is one of the diseases that more cause blindness worldwide. The Brazilian Council of Ophthalmology (CBO) estimates that in Brazil there are 985,000 people with glaucoma over 40 years old. The use of CAD and CADxsystems has contributed to increase the chances of detection and correct diagnoses, they provide, helping specialists inmaking decisions on glaucoma treatment. OBJECTIVE: To introduce a method for diagnosing glaucoma in fundus imageusing the LBP to represent the optic disk region, geostatistical functions to describe patterns and SVM to classify the images. METHODS: Run in 3 steps: Image representation (2), Feature extraction with geostatistic (3) and Classification and Validation (4). RESULTS: we obtained 88% specificity, 82% sensitivity and 84% accuracy in the diagnosis of glaucoma. CONCLUSION: The method has shown promise as a tool to aid the diagnosis of glaucoma.

Texture analysis of masses in digitized mammograms using Gleason and Menhinick diversity indexes

INTRODUCTION: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. METHODS: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms. To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. RESULTS: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. CONCLUSION: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.

Detection of masses based on asymmetric regions of digital bilateral mammograms using spatial description with variogram and cross-variogram functions.

A mammogram is an examination of the breast intended to prevent and diagnose breast cancer. In this work we propose a methodology for detecting masses by determining certain asymmetric regions between pairs of mammograms of the left and the right breast. The asymmetric regions are detected by means of structural variations between corresponding regions, defined by a spatial descriptor called cross-variogram function. After determining the asymmetric regions of a pair of images, the variogram function is applied to each asymmetric region separately, for classification as either mass or non-mass. The first stage of the methodology consists in preprocessing the images to make them adequate for registration. The following step performs the bilateral registration of pairs of left and right breasts. Pairs of corresponding regions are listed and their variations are measured by means of the cross-variogram spatial descriptor. Next, a model is created to train a Support Vector Machine (SVM) using the values of the cross-variogram function of each pair of windows as features. The pairs of breasts containing lesions are classified as asymmetric regions; the remaining ones are classified as symmetric regions. From the asymmetric regions, features are extracted from the variogram function to be used as tissue texture descriptors. The regions containing masses are classified as mass regions, and the other ones as non-mass regions. Stepwise linear discriminant analysis is used to select the most statistically significant features. Tests are performed with new cases for the final classification as either mass or non-mass by the trained SVM. The best results presented in the final classification were 96.38% of accuracy, 100% of sensitivity and 95.34% of specificity. The worst case presented 70.21% of accuracy, 100% of sensitivity and 67.56% of specificity. The average values for all tests were 90.26% of accuracy, 100% of sensitivity and 85.37% of specificity.

Royal sun medicinal mushroom Agaricus brasiliensis (higher Basidiomycetes) and the attenuation of pulmonary inflammation induced by 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK).

Agaricus brasiliensis currently is one of the most studied fungi because of its nutritional and therapeutic properties as an anti-inflammatory agent and an adjuvant in cancer chemotherapy. The effects of orally administered aqueous A. brasiliensis extract (14.3- and 42.9-mg doses) on parenchymal lung damage induced by carcinogenic 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) were observed in Wistar rats. NNK treatment induced pulmonary inflammation, but not lung cancer, in the rats. The lungs of animals treated with NNK showed a higher level of inflammation than those of the control group according to histopathologic examinations (P < 0.01) and kurtosis analysis (P < 0.001) of a global histogram generated from thoracic computed tomography scans. There was no significant difference in the alveolar and bronchial exudates between animals treated with a 14.3-mg dose of A. brasiliensis extract and the control without NNK. However, a significant difference was found between animals treated with NNK, received a 42.9-mg dose of A. brasiliensis (P < 0.05), and the controls not treated with NNK. We did not observe a significant difference between the kurtoses of the A. brasiliensis (14.3 mg) and control groups. However, a 42.9-mg dose of A. brasiliensis resulted in lower kurtosis values than those observed in the control group (P < 0.001). In conclusion, a low dose of A. brasiliensis was more effective in attenuating pulmonary inflammation. Similar to the histopathological results, the computed tomography scans also showed a protective effect of A. brasiliensis at the lower dose, which prevented gross pulmonary consolidation.

Relationship between the phase angle and volume of tumours in patients with lung cancer.

BACKGROUND/AIMS: The phase angle (PA) obtained by bioelectrical impedance has been used as a predictor of nutritional status in cancer. This study aimed to verify the association between the PA and tumour volume in non-small cell lung cancer (NSCLC) patients. METHODS: Volumetric determination of the tumour mass was performed using a computerised image analysis system incorporated in helical tomography. Lesion segmentation was performed by a semi-automatic process using a region growth algorithm with voxel aggregation. The PA was measured by bioelectrical impedance. RESULTS: A total of 30 male patients with a mean age of 65.6 years were evaluated. The mean values observed for body mass index, PA and tumour volume were 22.5 ± 4.19, 5.66 ± 0.9° and 163.2 ± 207.5 ml, respectively. The tumour volumes were negatively correlated with the PA (r = -0.55; p < 0.001) and positively correlated with the ratio between the extracellular mass and the body cell mass (ECM/BCM) (r = 0.59; p < 0.001). In multivariate analysis, independent predictors for both PA and ECM/BCM were tumour volume and Karnofsky performance status score. CONCLUSIONS: In NSCLC, the PA is closely associated with tumour volume, which may be important in early nutritional intervention.

Computational methodology for automatic detection of strabismus in digital images through Hirschberg test.

Strabismus is a pathology that affects about 4% of the population, causing aesthetic problems, reversible at any age; however, problems that can also cause irreversible muscular alterations, and alter the vision mechanism. The Hirschberg test is one of the exams used to detect this pathology. The application of high technology resources to help diagnose and treat ophthalmological conditions is, lamentably, not commonly found in the sub-specialty of strabismus. This work presents a methodology for automatic detection of strabismus in digital images through the Hirschberg test. For such, the work was organized into four stages: (1) finding the region of the eyes; (2) determining the precise location of the eyes; (3) locating the limbus and brightness; and (4) identifying strabismus. The methodology has produced results on the range of 100% sensibility, 91.3% specificity and 94% for the correct identification of strabismus, ensuring the efficiency of its geostatistical functions for the extraction of eye texture and for the calculation of the alignment between the eyes on digital images obtained from the Hirschberg test.

Detection of masses in mammogram images using CNN, geostatistic functions and SVM.

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87.

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 patient's 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%.

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 Ripley's 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 Ripley's 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 discriminator's 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.