Prostate cancer spectral multifeature analysis using TRUS images

This paper focuses on extracting and analyzing different spectral features from transrectal ultrasound (TRUS) images for prostate cancer recognition. First, the information about the images' frequency domain features and spatial domain features are combined using a Gabor filter and then integrated with the expert radiologist's information to identify the highly suspicious regions of interest (ROIs). The next stage of the proposed algorithm is to scan each identified region in order to generate the corresponding 1-D signal that represents each region. For each ROI, possible spectral feature sets are constructed using different new geometrical features extracted from the power spectrum density (PSD) of each region's signal. Next, a classifier-based algorithm for feature selection using particle swarm optimization (PSO) is adopted and used to select the optimal feature subset from the constructed feature sets. A new spectral feature set for the TRUS images using estimation of signal parameters via rotational invariance technique (ESPRIT) is also constructed, and its ability to represent tissue texture is compared to the PSD-based spectral feature sets using the support vector machines (SVMs) classifier. The accuracy obtained ranges from 72.2% to 94.4%, with the best accuracy achieved by the ESPRIT feature set.     

Pubic arch detection in transrectal ultrasound guided prostatecancer therapy

New biopsy techniques, increased life expectancy, and prostate-specific antigen (PSA) screening have contributed to an increase in the reported incidence of prostate cancer. Among several treatment options available to the patients, transperineal prostate brachytherapy has emerged as a medically successful, cost-effective outpatient procedure for treating localized prostate cancer. Transperineal prostate brachytherapy employs transrectal ultrasound (TRUS) as the primary imaging modality to accurately preplan and subsequently execute the placement of radioactive seeds into the prostate. Under TRUS guidance, a needle (preloaded with radioactive seeds) is inserted through a template guide, through the perineum and into a predetermined prostate target. The pubic arch, formed by the central union of pelvic bones, is a potential barrier to the passage of these needles in the prostate. A critical aspect, therefore, in the planning and execution of the brachytherapy procedure is the accurate assessment of pubic arch interference (PAI) in relation to the prostate. Traditionally, the evaluation of PAI has involved computed tomography correlate scanning or crude subjective evaluations. In this paper, the authors describe a new method of assessing PAI by detecting the pubic arch via image processing on the TRUS images. The PAI detection (PAID) algorithm first uses a technique known as sticks to selectively enhance the contrast of linear features in ultrasound images. Next, the enhanced image is thresholded via percentile thresholding. Finally, the authors fit a parabola (a model for the pubic arch) recursively to the thresholded image. Their evaluation result from 15 cases indicates that the algorithm can successfully detect the pubic arch with 90% accuracy. Based on this study, the authors believe that detecting the pubic arch and assessing PAI can be done practically and more accurately in the clinical setting using TRUS rather than the current available methods     

Modelling prostate motion for data fusion during image-guided interventions

There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.     

A 3-D reconstruction algorithm for interpolation and extrapolation of planar cross sectional data

A three dimensional (3-D) reconstruction algorithm utilizing both linear interpolation and linear extrapolation was developed for the study of human prostatic cancer. The algorithm was validated by comparing the volumes and shapes of original to reconstructed objects. Synthetic objects of known geometry and wax models with shapes characteristic of prostatic carcinomas were assessed with standard planimetry and by the digital interpolation-extrapolation method. Volume and multifocality measurements obtained by reconstructing excised prostate glands using histologic maps obtained from whole-mount sections were tested. The new algorithm provided greater accuracy in determining tumor volumes than conventional methods. This model provides a basis for mathematical analysis of prostate cancer lesions.     

Adaptively learning local shape statistics for prostate segmentation in ultrasound

Automatic segmentation of the prostate from 2-D transrectal ultrasound (TRUS) is a highly desired tool in many clinical applications. However, it is a very challenging task, especially for segmenting the base and apex of the prostate due to the large shape variations in those areas compared to the midgland, which leads many existing segmentation methods to fail. To address the problem, this paper presents a novel TRUS video segmentation algorithm using both global population-based and patient-specific local shape statistics as shape constraint. By adaptively learning shape statistics in a local neighborhood during the segmentation process, the algorithm can effectively capture the patient-specific shape statistics and quickly adapt to the local shape changes in the base and apex areas. The learned shape statistics is then used as the shape constraint in a deformable model for TRUS video segmentation. The proposed method can robustly segment the entire gland of the prostate with significantly improved performance in the base and apex regions, compared to other previously reported methods. Our method was evaluated using 19 video sequences obtained from different patients and the average mean absolute distance error was 1.65 ± 0.47 mm.     

MRI-Compatible Pneumatic Robot for Transperineal Prostate Needle Placement

Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of prostate and surrounding tissue, thus granting potential to be a superior medical imaging modality for guiding and monitoring prostatic interventions. However, the benefits cannot be readily harnessed for interventional procedures due to difficulties that surround the use of high-field (1.5T or greater) MRI. The inability to use conventional mechatronics and the confined physical space makes it extremely challenging to access the patient. We have designed a robotic assistant system that overcomes these difficulties and promises safe and reliable intraprostatic needle placement inside closed high-field MRI scanners. MRI compatibility of the robot has been evaluated under 3T MRI using standard prostate imaging sequences and average SNR loss is limited to 5%. Needle alignment accuracy of the robot under servo pneumatic control is better than 0.94 mm rms per axis. The complete system workflow has been evaluated in phantom studies with accurate visualization and targeting of five out of five 1 cm targets. The paper explains the robot mechanism and controller design, the system integration, and presents results of preliminary evaluation of the system.     

Novel round-robin tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery.

Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.     

Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer

In this study, we registered live-time interventional magnetic resonance imaging (iMRI) slices with a previously obtained high-resolution MRI volume that in turn can be registered with a variety of functional images, e.g., PET, SPECT, for tumor targeting. We created and evaluated a slice-to-volume (SV) registration algorithm with special features for its potential use in iMRI-guided radio-frequency (RF) thermal ablation of prostate cancer. The algorithm features included a multiresolution approach, two similarity measures, and automatic restarting to avoid local minima. Imaging experiments were performed on volunteers using a conventional 1.5-T MR scanner and a clinical 0.2-T C-arm iMRI system under realistic conditions. Both high-resolution MR volumes and actual iMRI image slices were acquired from the same volunteers. Actual and simulated iMRI images were used to test the dependence of SV registration on image noise, receive coil inhomogeneity, and RF needle artifacts. To quantitatively assess registration, we calculated the mean voxel displacement over a volume of interest between SV registration and volume-to-volume registration, which was previously shown to be quite accurate. More than 800 registration experiments were performed. For transverse image slices covering the prostate, the SV registration algorithm was 100% successful with an error of <2 mm, and the average and standard deviation was only 0.4 mm +/- 0.2 mm. Visualizations such as combined sector display and contour overlay showed excellent registration of the prostate and other organs throughout the pelvis. Error was greater when an image slice was obtained at other orientations and positions, mostly because of inconsistent image content such as that from variable rectal and bladder filling. These preliminary experiments indicate that MR SV registration is sufficiently accurate to aid image-guided therapy.     

Computation and visualization of three-dimensional soft tissue motion in the orbit

This work presents a method to measure the soft tissue motion in three dimensions in the orbit during gaze. It has been shown that two-dimensional (2-D) quantification of soft tissue motion in the orbit is effective in the study of orbital anatomy and motion disorders. However, soft tissue motion is a three-dimensional (3-D) phenomenon and part of the kinematics is lost in any 2-D measurement. Therefore, T1-weighted magnetic resonance (MR) imaging volume sequences are acquired during gaze and soft tissue motion is quantified using a generalization of the Lucas and Kanade optical flow algorithm to three dimensions. New techniques have been developed for visualizing the 3-D flow field as a series of color-texture mapped 2-D slices or as a combination of volume rendering for display of the anatomy and scintillation rendering for the display of the motion field. We have studied the performance of the algorithm on four-dimensional volume sequences of synthetic motion, simulated motion of a static object imaged by MR, an MR-imaged rotating object and MR-imaged motion in the human orbit during gaze. The accuracy of the analysis is sufficient to characterize motion in the orbit and scintillation rendering is an effective visualization technique for 3-D motion in the orbit.     

Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics

Prostate brachytherapy quality assessment procedure should be performed while the patient is still on the operating table since this would enable physicians to implant additional seeds immediately into the prostate if necessary thus reducing the costs and increasing patient outcome. Seed placement procedure is readily performed under fluoroscopy and ultrasound guidance. Therefore, it has been proposed that seed locations be reconstructed from fluoroscopic images and prostate boundaries be identified in ultrasound images to perform dosimetry in the operating room. However, there is a key hurdle that needs to be overcome to perform the ultrasound and fluoroscopy-based dosimetry: it is highly time-consuming for physicians to outline prostate boundaries in ultrasound images manually, and there is no method that enables physicians to identify three-dimensional (3-D) prostate boundaries in postimplant ultrasound images in a fast and robust fashion. In this paper, we propose a new method where the segmentation is defined in an optimization framework as fitting the best surface to the underlying images under shape constraints. To derive these constraints, we modeled the shape of the prostate using spherical harmonics of degree eight and performed statistical analysis on the shape parameters. After user initialization, our algorithm identifies the prostate boundaries on the average in 2 min. For algorithm validation, we collected 30 postimplant prostate volume sets, each consisting of axial transrectal ultrasound images acquired at 1-mm increments. For each volume set, three experts outlined the prostate boundaries first manually and then using our algorithm. By treating the average of manual boundaries as the ground truth, we computed the segmentation error. The overall mean absolute distance error was 1.26 +/- 0.41 mm while the percent volume overlap was 83.5 +/- 4.2. We found the segmentation error to be slightly less than the clinically-observed interobserver variability.     

Semantic image retrieval in magnetic resonance brain volumes

Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to study disease progression and to correlate observations across multiple subjects. In this paper, a novel technique for retrieving 2-D MR images (slices) in 3-D brain volumes is proposed. Given a 2-D MR query slice, the technique identifies the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain, and retrieves the matching slice within this region in the identified volumes. The proposed technique is capable of retrieving an image in multimodal and noisy scenarios. In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions. In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based retrieval framework is developed. The proposed retrieval technique is tested on various modalities. The test results reveal superior robustness performance with respect to accuracy, speed, and multimodality.     

Three-dimensional segmentation and interpolation of magnetic resonance brain images

The authors propose a method for the 3-D reconstruction of the brain from anisotropic magnetic resonance imaging (MRI) brain data. The method essentially consists in two original algorithms both for segmentation and for interpolation of the MRI data. The segmentation process is performed in three steps. A gray level thresholding of the white and gray matter tissue is performed on the brain MR raw data. A global white matter segmentation is automatically performed with a global 3-D connectivity algorithm which takes into account the anisotropy of the MRI voxel. The gray matter is segmented with a local 3-D connectivity algorithm. Mathematical morphology tools are used to interpolate slices. The whole process gives an isotropic binary representation of both gray and white matter which are available for 3-D surface rendering. The power and practicality of this method have been tested on four brain datasets. The segmentation algorithm favorably compares to a manual one. The interpolation algorithm was compared to the shaped-based method both quantitatively and qualitatively.     

Medical image compression by using three-dimensional wavelet transformation

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance.     

Three-dimensional ultrasound imaging systems for prostate cancerdiagnosis and treatment

3D ultrasound imaging system for imaging the prostate can be interfaced to any conventional ultrasound machine, and can accommodate side-firing transrectal ultrasound transducers. After acquiring a series of 2D ultrasound images, a 3D image is reconstructed. The 3D image is available to the physician, allowing the prostate to be viewed interactively in multiple simultaneous planes, allowing better visualization of its internal architecture. This approach allows the physician to record and view the whole prostate in successive examinations, making 3D TRUS well-suited to performing prospective or follow-up studies. The results indicate that 3D ultrasound imaging of the prostate has great potential as a tool for the diagnosis and follow-up of prostate disease     

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.     

A novel MR-conditional cam-based automatic prostate biopsy device

Biopsy is the most commonly used method for diagnosing prostate cancer, in which a biopsy gun is used to obtain the tissue samples. The outstanding performance of magnetic resonance imaging (MRI) for soft tissue can provide better medical guidance for prostate biopsy. This paper reports an automatic biopsy device that can be used in the MRI environment and that is expected to be used together with the MR conditional robotic prostate biopsy system. This paper proposes a compact cam-based automatic biopsy device that can perform all actions required for prostate biopsy sampling procedure using a single actuator. Its design concept, implementation, required force analysis, sampling performance evaluation, and MR compatibility evaluation are also provided. The experimental evaluation showed that the tissue sampling process can be easily and completely performed using the proposed automatic biopsy device, and that its sampling performance is comparable to that of the existing commercially available manual biopsy gun. MR compatibility test showed that an 18.7% reduction in the signal-to-noise ratio when the device was operating.     

Automatic brachytherapy seed placement under MRI guidance

The paper presents a robotic method of performing low dose rate prostate brachytherapy under magnetic resonance imaging (MRI) guidance. The design and operation of a fully automated MR compatible seed injector is presented. This is used with the MrBot robot for transperineal percutaneous prostate access. A new image-registration marker and algorithms are also presented. The system is integrated and tested with a 3T MRI scanner. Tests compare three different registration methods, assess the precision of performing automated seed deployment, and use the seeds to assess the accuracy of needle targeting under image guidance. Under the ideal conditions of the in vitro experiments, results show outstanding image-guided needle and seed placement accuracy.     

An in vivo analysis of the motion of the peri-talar joint complex based on MR imaging

The purpose of this work is to characterize the three-dimensional (3-D) motion of the peritalar joint complex in vivo using magnetic resonance imaging (MRI). Each image data set utilized in this study is made of 60 longitudinal MR slices of the foot in each of eight positions from extreme pronation to extreme supination. We acquired and analyzed ten such data sets from normal subjects, seven data sets from pathological joints and two postoperative data sets. We segmented and formed the surfaces of the calcaneus, talus, cuboid and navicular from all data sets. About 30 geometrical parameters are computed for each joint in each position. The results present features of normal motion and show how normal and abnormal motion can be distinguished. They also show the consequences of surgery on the motion. This non- invasive method offers a unique tool to characterize and quantify the 3-D motion of the rearfoot in vivo from MR images.     

A comprehensive cardiac motion estimation framework using both untagged and 3-D tagged MR images based on nonrigid registration

In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields.