Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules

A Fourier-Based Algorithm for Micro-Calcification Enhancement in Mammographic Images

Breast cancer is the most widespread cancer in women in the world; it manifests mostly in two forms: microcalcifications and massive lesions. These two forms differ in density, size, shape and number. Consequently, there are two different kinds of mammographic CAD algorithms: those for microcalcifications detection, and those for massive lesions detection. The microcalcifications detection is a hard task, since they are quite small and often poorly contrasted against the background, especially in images affected by digitization noise. In a CAD system the ROI Hunter plays an important role, because missed microcalcifications at this level are definitely lost. For this reason, highlighting methods for suspected microcalcifications may be useful in a CAD system. In this work, we describe a Fourier Transform based microcalcifications enhancement method, which takes place after the image preprocessing and before the ROI Hunter step, aimed at highlighting suspected area

Computer-aided diagnosis in digital mammography: comparison of two commercial systems

Aim: Within this work, a comparative analysis of two commercial computer-aided detection or diagnosis (CAD) systems, CyclopusCAD® mammo (v. 6.0) produced by CyclopusCAD Ltd (Palermo, Italy) and SecondLook® (v. 6.1C) produced by iCAD Inc. (OH, USA) is performed by evaluating the results of both systems application on an unique set of mammographic digital images routinely acquired in a hospital structure. Materials & methods: The two CAD systems have been separately applied on a sample set of 126 mammographic digital cases, having been independently diagnosed by two senior radiologists. According to the human diagnosis, the cases in the sample reference set are divided into 61 negatives and 65 pathological cases (21 cases displaying both mass lesions and microcalcifications and 44 cases characterized only by mass lesions). The images in the pathological subset contain 123 human diagnosed mass lesions and 37 human diagnosed microcalcifications clusters. In the case of CyclopusCAD, the system offered the possibility to evaluate sensitivity at several threshold levels (working points); five different setting levels (high sensitivity, normal sensitivity, standard, normal specificity and high specificity) have been used. Results: At the standard threshold level, CyclopusCAD exhibits an overall sensitivity of 83.1 versus 66.2% for iCAD (p = 0.04) and an average number of false positives per image (FP/im) of 1.38 against 0.47 for iCAD (p < 0.01). Specifically, for the mass lesions, CyclopusCAD exhibits a sensitivity of 76.9% at a rate of 0.73 FP/im, while iCAD displays a sensitivity of 61.5% at 0.28 FP/im. For the microcalcifications, CyclopusCAD exhibits a sensitivity of 76.2% at a rate of 0.64 FP/image, while iCAD displays a sensitivity of 61.9% at 0.19 FP/im. The reported results have also been expressed in terms of free-response receiver operating characteristic curves, corresponding to five different thresholds in the case of CyclopusCAD and to one single threshold value for iCAD. Conclusion: The overall accuracies of the two systems are fairly comparable up to the uncertainty level of this analysis. CyclopusCAD may reach a higher sensitivity level for both masses and microcalcifications owing to the flexibility in the working point choice, with the price of a major number of FP/im

Fast Fourier Transform Filtering for Bilateral Mammography Comparison

Bilateral Asymmetry is one of the breast abnormalities that may indicate a cancer in early stage. The computer methods for the bilateral subtraction developed up to now show the problem of large false positives number because the alignment defects. On the other hand the computer methods using FFT approach suffer of a low S/N ratio to distinguish massive lesions from background. In this paper a method (FFT-RF-BMC) is presented to enhanche the bilateral asymmetry using a FFT to detect massive lesions through a Recursive Filtering

A Method to Reduce the FP/imm Number Through CC and MLO Views Comparison in Mammographic Images

In this paper we propose a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient. The proposed solution uses the symmetry properties of the breast to compute a geometric transformation that permits to represent the two images in comparable coordinates systems. Through this method, potential pathological ROIs of one of the projections are correlated with the ROIs in the second view. To show the effectiveness of the result we apply the method on a dataset composed of 112 couples of pathological images. Experiments shows that method enables a reduction by up to 700/0 of the FP/imm number detected after the classification ste