POINTS CLASSIFICATION BY A SEQUENTIAL HIGHER - ORDER MOMENTS STATISTICAL ANALYSIS OF LIDAR DATA

The paper deals with a new sequential procedure to perform unsupervised LIDAR points classification by iteratively studying skewness and kurtosis for elevation and intensity point distribution values. After a preliminary local shape analysis of elevation and intensity point distributions, carried out from the original discrete frequencies by a non parametric estimation of the density functions, the procedure starts by choosing the category of data (elevation or intensity) to analyse at first: the choice falls on the category better showing by a testing procedure a bi or a multi clustering distribution. The first point cluster is identified by studying the distribution skewness and kurtosis variations, after removing at each step the largest data values. The selected cluster is furthermore analysed by studying higher order moments behaviour of the complementary data category. This makes possible to find out potential sub clusters of the original selected one, permitting, in this way, a more effective point classification. Successive clusters are identified by applying the same iterative procedure to the still unclassified LIDAR points. For complex point distribution shapes or for the classification of large areas, a progressive analysis method, based on the partition of the entire data set into regular subsets, is proposed. Some real numerical experiments confirm the capability of the method proposed. The classification total errors in the experiments range from a minimum value of 1,2% to a maximum value of 8,9%

LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories

A new procedure supporting filtering and classification of LiDAR data based on both elevation and intensity analysis is introduced and validated. After a preliminary analysis to avoid the trivial classification of homogeneous datasets, a non-parametric estimation of the probability density function is computed for both elevation and intensity data values. Some statistical tests are used for selecting the category of data (elevation or intensity) that better satisfies a bi- or a multi-modal distribution. The iterative analysis of skewness and kurtosis is then applied to this category to obtain a first classification. At each step, the point with the highest value of elevation (or intensity) is removed. The classification is then refined by studying both statistical moments of the complementary data category, in order to look for potential sub-clusters. Remaining clusters are identified by applying the same iterative procedure to the still unclassified LiDAR points. For more complex point distribution shapes or for the classification of large scenes, a progressive analysis is proposed, which is based on the partitioning of the entire dataset into more sub-sets. Each of them is then independently classified by using the core procedure. Some numerical experiments on real LiDAR data confirmed the potentiality of the filtering/classification method

Points classification by a sequential higher - order moments statistical analysis of LIDAR data

The paper deals with a new sequential procedure to perform unsupervised LIDAR points classification by iteratively studying skewness and kurtosis for elevation and intensity point distribution values. After a preliminary local shape analysis of elevation and intensity point distributions, carried out from the original discrete frequencies by a non parametric estimation of the density functions, the procedure starts by choosing the category of data (elevation or intensity) to analyse at first: the choice falls on the category better showing by a testing procedure a bi or a multi clustering distribution. The first point cluster is identified by studying the distribution skewness and kurtosis variations, after removing at each step the largest data values. The selected cluster is furthermore analysed by studying higher order moments behaviour of the complementary data category. This makes possible to find out potential sub clusters of the original selected one, permitting, in this way, a more effective point classification. Successive clusters are identified by applying the same iterative procedure to the still unclassified LIDAR points. For complex point distribution shapes or for the classification of large areas, a progressive analysis method, based on the partition of the entire data set into regular subsets, is proposed. Some real numerical experiments confirm the capability of the method proposed. The classification total errors in the experiments range from a minimum value of 1,2% to a maximum value of 8,9%

POINTS CLASSIFICATION BY A SEQUENTIAL HIGHER – ORDER MOMENTS STATISTICAL ANALYSIS OF LIDAR DATA

The paper deals with a new sequential procedure to perform unsupervised LIDAR points classification by iteratively studying skewness and kurtosis for elevation and intensity point distribution values. After a preliminary local shape analysis of elevation and intensity point distributions, carried out from the original discrete frequencies by a non parametric estimation of the density functions, the procedure starts by choosing the category of data (elevation or intensity) to analyse at first: the choice falls on the category better showing by a testing procedure a bi or a multi clustering distribution. The first point cluster is identified by studying the distribution skewness and kurtosis variations, after removing at each step the largest data values. The selected cluster is furthermore analysed by studying higher order moments behaviour of the complementary data category. This makes possible to find out potential sub clusters of the original selected one, permitting, in this way, a more effective point classification. Successive clusters are identified by applying the same iterative procedure to the still unclassified LIDAR points. For complex point distribution shapes or for the classification of large areas, a progressive analysis method, based on the partition of the entire data set into regular subsets, is proposed. Some real numerical experiments confirm the capability of the method proposed. The classification total errors in the experiments range from a minimum value of 1,2% to a maximum value of 8,9%

The imaging diagnosis of hepatic focal nodular hyperplasia

Focal nodular hyperplasia (FNH) is a rare benign hepatocellular tumor occurring in noncirrhotic patients, mostly females, 20-50 years of age. It is usually asymptomatic. The authors took the lead from 5 cases of FNH studied over last year to analyze the different patterns exhibited by the condition on the various imaging techniques currently available. At scintigraphy with 99mTc DISIDA or with TcSC, FNH can be hyper, normal, or hypocaptating. On US scans, the lesion is often homogeneous and isoechoic, but it can also be hyper/hypoechoic. With Doppler US, high-flow signals can be observed. On unenhanced CT scans the lesion is solid, well-demarcated, isodense or slightly hyperdense; sometimes it shows a central hypodense area corresponding to fibrovascular scar. On postcontrast scans it appears hyper/isodense. At dynamic CT the lesion density, which is high during the arterial phase, decreases quickly in the parenchymal and the venous phases and reaches equal/inferior values to surrounding liver parenchyma. On liver angio-CT it is sometimes possible to visualize the bile ducts in the central scar. At angiography, FNH is hypervascular and homogeneous. On MR scans, in T1-weighted SE sequences, the condition is isointense or slightly hypointense, whereas on T2-weighted pulse sequences it is slightly hyperintense; the central scar is hypointense on T1, and hyperintense on T2, weighted scans. As we have no pathognomonic patterns but only orientative ones, a reliable differential diagnosis with hepatocellular adenoma (HA) and fibrolamellar hepatocellular carcinoma (FL-HCC) must be based on biopsy or cytology or, even better, histology. The differential diagnosis is nevertheless necessary because, while FNH does not usually require a surgical approach but only a radiological follow-up, both HA (due to possible bleeding and degeneration) and FL-HCC require surgery