Dimensional Wavelet Transform Research Articles

Abstract 555: Examination and evaluation of MR radiomics features for characterization of dominant intraprostatic lesions

Abstract Purpose: This pilot study investigates a set of radiomics features extracted from fast relaxation fast spin echo (FRFSE) T2 pulse sequences for normal tissue and Dominant Intraprostatic Lesions (DILs) in twenty prostate cancer patients. Material and Methods: Twenty patients with prostate cancer were studied. All patients had axial FSRFSE T2 scans using a 3 Tesla scanner. A radiologist interpreted MR examinations, and contoured the suspicious DIL and the contralateral section of the prostate gland (normal) on the T2 weighted MR images. Patients underwent a 14-core transrectal Ultrasound Guided Biopsy and localization of positive cores, Gleason score and clinical tumor stage were recorded. 167 radiomics features were extracted from normal and DIL zones. These features were categorized into 8 different sets as following: Intensity Histogram Based (IHB), Gray Level Run Length (GLRL), Law’s Textural Information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), Two Dimensional Wavelet Transform (2DWT), Two Dimensional Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM) with 8, 7, 18, 18, 6, 48, 40, and 22 features in each category respectively. A Welch’s test and the Fisher method were used to test for significant differences among the 167 radiomics features and their subcategories. For all patients, correlation coefficients between the extracted features in the normal and DIL zones were also calculated. Results: According to the Fisher combined p-values, among the eight categories of radiomics features, only 5 feature categories showed a significant difference (IHB, GLRL, DOST, LBPF and GLCM with pFisher= 2.0x10-6, 0.02, 12 x10-4, 3.7 x10-3, and 1.5 x10-6 respectively). Among all 167 features, only 7 showed a significant difference (D=100x[DIL/NP-1]) and small correlation between normal and DIL zones: IHB-Skewness (r=0.19, p=0.03, and D=50.3%), GLCM-Contrast (r=0.12, p=0.03, and D=-67.5%), GLCM-Dissimilarity (r=0.12, p=0.01, and D=-67.5%), GLCM-Entropy (r=0.07, p=0.01, and D=-67.1%), GLCM-Difference-Variance (r=0.12, p=0.01, and D=-67.1%), GLCM-Difference-Entropy (r=0.10, p=0.01, and D=-60.4%), and GLCM-Information-Measure-of-Correlation (r=0.25, p=0.01, and D=-65.1%). Conclusion and Discussion: This pilot study demonstrates the feasibility of using radiomics features from MR images to characterize DILs in prostate cancer patients. Among 167 radiomics features extracted from axial MR T2 FRFSE, 7 features were shown to be potentially significant for distinguishing normal tissue from DILs. This research supports an integrated decision making system, combining clinical factors and radiomics features extracted from MR images, for increasing the DIL detection performance in prostate cancer studies. Citation Format: Hassan Bagher-Ebadian, Janic Branislava, Chang Liu, Milan Pantelic, David Hearshen, Indrin Chetty, Mohamed Elshaikh, Benjamin Movsas, Ning Wen. Examination and evaluation of MR radiomics features for characterization of dominant intraprostatic lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 555. doi:10.1158/1538-7445.AM2017-555

Embedded Object Detection with Radar Echo Data by Means of Wavelet Analysis of MRA: Multi-Resolution Analysis

A method for embedded object detection with radar echo data by means of wavelet analysis of MRA: Multi- Resolution Analysis, in particular, three dimensional wavelet transformations is proposed. In order to improve embedded object detecting capability, not only one dimensional radar echo data but also three dimensional data are used. Through a comparison between one dimensional edge detection with Sobel operator and three dimensional wavelet transformation based edge detection, it is found that the proposed method is superior to the Sobel operator based method

B203 ウェーブレット解析に基づく地中パルスレーダデータを用いた埋設物検出

A method for embedded object detection with radar echo data by means of wavelet analysis of MRA: Multi-Resolution Analysis, in particular, three dimensional wavelet transformation is proposed. In order to improve embedded object detecting capability, not only one dimensional radar echo data but also three dimensional data are used. Through a comparison between one dimensional edge detection with Sobel operator and three dimensional wavelet tarnsformation based edge detection, it is found that the proposed method is superior to the Sobel operator based method.

Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes

Abstract. Multi sensor data available through remote sensing satellites provide information about changes in the state of the oceans, land and atmosphere. Recent studies have shown anomalous changes in oceans, land, atmospheric and ionospheric parameters prior to earthquakes events. This paper introduces an innovative data mining technique to identify precursory signals associated with earthquakes. The proposed methodology is a multi strategy approach which employs one dimensional wavelet transformations to identify singularities in the data, and an analysis of the continuity of the wavelet maxima in time and space to identify the singularities associated with earthquakes. The proposed methodology has been employed using Surface Latent Heat Flux (SLHF) data to study the earthquakes which occurred on 14 August 2003 and on 1 March 2004 in Greece. A single prominent SLHF anomaly has been found about two weeks prior to each of the earthquakes.

210 極座標表現Waveletによる衛星画像からの水塊特徴の抽出

The conventional two dimensional wavelet transformation is not effective to extract arc and circle features form the imagery data. In order to enhance the capability in an effective manner, a polar coordinates of representation of wavelet transformations is proposed. The proposed paper describes the proposed method of the aforementioned polar coordinates of representation of wavelet transformations followed by examples of the application of the method for circle feature extraction from Earth observation satellite data of OCTS onboard ADEOS satellite. The results are compared to the conventional two dimensional wavelet transfromations.