Eliko Tehnoloogia Arenduskeskus

<p>This paper proposes a heading drift correction algorithm for inertial and positioning sensor fusion for indirect localization of industrial products in warehouse management. The tracking of objects is performed indirectly during their transportation by constantly tracked industrial machinery (e.g., forklift). The fusion of inertial and positioning units provide real-time information on position and heading of industrial machinery, which is transformed into real-time position of the carried payload. Within this work, a test setup was assembled and based on the proposed inertial measurement unit (IMU) heading correction algorithm. The performance of the solution was assessed in an industrial production environment. Results show that the proposed algorithm was able to reduce the heading median error from 44.1 degrees to 5.9 degrees, which is 86.5% improvement in measured heading accuracy. This results in an improvement of the positioning accuracy from 1.91 m median error to 0.26 m median error for the indirectly tracked object.</p>

Currently reported computer-aided detection (CAD) approaches face difficulties in identifying the diverse pulmonary nodules in thoracic computed tomography (CT) images, especially in heterogeneous datasets. We present a novel CAD system specifically designed to identify multisize nodule candidates in multiple heterogeneous datasets. The proposed CAD scheme is divided into two phases: primary phase and final phase. The primary phase started with the lung segmentation algorithm and the segmented lungs were further refined using morphological closing process to include the pleural nodules. Next, we empirically formulated three subalgorithms modules to detect different sizes of nodule candidates (≥3 and <6mm; ≥6 and <10mm; and ≥10mm). Each subalgorithm module included a multistage flow of rule-based thresholding and morphological processes. In the final phase, the nodule candidates were augmented to boost the performance of the classifier. The CAD system was trained using a total number of nodule candidates=201,654 (after augmentation) and nonnodule candidates=731,486. A rich set of 515 features based on cluster, texture, and voxel-based intensity features were utilized to train a neural network classifier. The proposed method was trained on 899 scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC-IDRI). The CAD system was also independently tested on 153 CT scans taken from the AAPM-SPIE-LungX Dataset and two subsets from the Early Lung Cancer Action Project (ELCAP and PCF). For the LIDC-IDRI training set, the proposed CAD scheme yielded an overall sensitivity of 85.6% (1189/1390) and 83.5% (1161/1390) at 8 FP/scan and 1 FP/scan, respectively. For the three independent test sets, the CAD system achieved an average sensitivity of 68.4% at 8 FP/scan. The authors conclude that the proposed CAD system can identify dissimilar nodule candidates in the multiple heterogeneous datasets. It could be considered as a useful tool to support radiologists during screening trials.