Parvin Mousavi

Detection of Prostate Cancer from RF Ultrasound Echo Signals Using Fractal Analysis

In this paper we propose a new feature, average Higuchi dimension of RF time series (AHDRFT), for detection of prostate cancer using ultrasound data. The proposed feature is extracted from RF echo signals acquired from prostate tissue in an in vitro setting and is used in combination with texture features extracted from the corresponding B-scan images. In a novel approach towards RF data collection, we continuously recorded backscattered echoes from the prostate tissue to acquire time series of the RF signals. We also collected B-scan images and performed a detailed histopathologic analysis on the tissue. To compute AHDRFT, the Higuchi fractal dimensions of the RF time series were averaged over a region of interest. AHDRFT and texture features extracted from corresponding B-scan images were used to classify regions of interest, as small as 0.028 cm of the prostate tissue in cancerous and normal classes. We validated the results based on our histopathologic maps. A combination of image statistical moments and features extracted from co-occurrence matrices of the B-scan images resulted in classification accuracy of around 87%. When AHDRFT was added to the feature vectors, the classification accuracy was consistently over 95% with best results of over 99% accuracy. Our results show that the RF time series backscattered from prostate tissues contain information that can be used for detection of prostate cancer

Feature analysis and classification of chromosome 16 homologs using fluorescence microscopy images

Image processing techniques used to classify human chromosome 16 into two classes of parental homologs are described. The classification is accomplished using DNA probes and detecting intensity differences in homologs of chromosome 16. The classification of homologous chromosomes into maternal and paternal classes is essential to advanced studies of cancer genetics.

Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm

Classification of homologous chromosomes is essential to advanced studies of cancer genetics. Centromere intensities are believed to be an important differentiating feature between homologs. Therefore, segmentation of centromeres is a major step toward the realization of homolog classification. This paper describes an iterative fuzzy algorithm which successfully segments centromeres from images of human chromosomes prepared using fluorescence in-situ hybridization technique. The algorithm is based on assigning a fuzzy membership value to each pixel in the centromere image. An iterative algorithm then updates and minimizes a defined error function. Chromosome 22, a highly heteromorphic chromosome, is used to verify the centromere segmentation method. Homologs of this chromosome are classified based on their segmented centromere intensities as well as their morphological differences. The classification results of these two methods agree completely and are used to validate our developed algorithm.

Discrete Fourier Analysis of Ultrasound RF Time Series for Detection of Prostate Cancer

In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform of ultrasound radio-frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF time series refer to a series of echoes received from one spatial location of tissue while the imaging probe and the tissue are fixed in position. Our previous investigations have shown that at least one feature, fractal dimension, of these signals demonstrates strong correlation with the tissue microstructure. In the current paper, six new features that represent the frequency spectrum of the RF time series have been used, in conjunction with a neural network classification approach, to detect prostate cancer in regions of tissue as small as 0.03 cm2. Based on pathology results used as gold standard, we have acquired mean accuracy of 91%, mean sensitivity of 92% and mean specificity of 90% on seven human prostates.

A navigation system for shoulder arthroscopic surgery

Abstract The general framework and experimental validation of a novel navigation system designed for shoulder arthroscopy are presented. The system was designed to improve the surgeons perception of the three-dimensional space within the human shoulder. Prior to surgery, a surface model of the shoulder was created from computed tomography images. Intraoperatively optically tracked arthroscopic instruments were calibrated. The surface model was then registered to the patient using tracked freehand ultrasound images taken from predefined landmark regions on the scapula. Three-dimensional models of the surgical instruments were displayed, in real time, relative to the surface model in a user interface. Laboratory experiments revealed only small registration and calibration errors, with minimal time needed to complete the intraoperative tasks.

Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series

The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchis approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features.

A new approach to analysis of RF ultrasound echo signals for tissue characterization: animal studies

We present the results of an animal tissue characterization study to demonstrate the effectiveness of a novel approach in collecting and analyzing ultrasound echo signals. In this approach, we continuously record RF echo signals backscattered from a tissue sample, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of RF signal samples. The Higuchi fractal dimension of the resulting time series at each spatial coordinate of the RF frame, averaged over a region of interest, serves as our tissue characterizing feature. The proposed feature is used along with Bayesian classifiers and feed-forward neural networks to distinguish different types of animal tissue. Pairwise classification of four different types of animal tissue are performed. Accuracies are in the range of 68%-96% and are significantly higher than the natural split of the data. The promising results of this study show that analysis of RF time series as proposed here, can potentially give rise to effective measures for ultrasound-based tissue characterization.

Identification of Anatomical Landmarks for Registration of CT and Ultrasound Images in Computer-Assisted Shoulder Arthroscopy

This paper presents a phantom study that was conducted for an ultrasound-guided shoulder arthroscopy navigation system. The navigation system uses a surface model generated from pre-operative computed tomography images, which has to be registered to the patient during the procedure. The goal of this study was to determine the optimal regions on the scapula bone of the shoulder to achieve an acceptable registration. Experiments were performed to examine the robustness and suitability of these optimal regions by testing the sensitivity to variations in the initial alignment for two different registration algorithms, namely iterative closest point and sequential least squares estimation technique. The fiducial registration error was analyzed and compared for all experiments. Regions spread over the entire scapula result in significantly smaller registration error (p<0.001) than regions, concentrated around the shoulder joint and thus accessible during the shoulder arthroscopy. However, the results also showed that the registration is still acceptable for the image- guided navigation system when these accessible landmarks are used

P3E-7 A New Feature For Detection Of Prostate Cancer Based On RF Ultrasound Echo Signals

In this paper we describe a new approach to tissue characterization for detection of prostate cancer. We propose that if a specific location in the prostate tissue undergoes continuous interactions with ultrasound, the time series of RF echo signals from that location would carry tissue characterizing information. This phenomenon is due to different microstructures of normal and cancerous tissues. We use Higuchis methodology to compute the fractal dimension of RF echo time series as a measure of the complexity. Averaged fractal dimension over a region of interest of the prostate tissue is utilized as the sole tissue characterizing feature and applied along with a Bayesian classifier. The results are validated based on detailed histopathologic maps of malignancy. The area under ROC curve is 0.894 and accuracies of up to 86% are acquired, indicating the effectiveness of our tissue characterization approach based on the fractal analysis of RF time series

Classification of homologous human chromosomes using mutual information maximization

Multi-feature analysis of human chromosome images is a major step towards classification of homologous chromosomes. An automatic quantitative classification method is proposed for homolog differentiation using multiple features. This method is based on mutual information maximization applied to an unsupervised neural network architecture. The neural network consists of separate modules which are trained to classify homologs using independent features. Mutual information is then maximized between the outputs of the modules forcing them to produce the same classification results, for a given chromosome. The proposed method was successfully applied to classify homologs of chromosome 16 with 100% accuracy.