Elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. We investigated a tissue elasticity measurement method using forward and inversion algorithms for the application of early breast tumor identification. An optical based elasticity measurement system is developed to capture images of the embedded lesions using total internal reflection principle. From elasticity images, we developed a novel method to estimate the elasticity of the embedded lesion using 3-D finite-element-model-based forward algorithm, and neural-network-based inversion algorithm. The experimental results showed that the proposed characterization method can be diffierentiate the benign and malignant breast lesions.
This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image’s local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.
In this paper, we propose novel feature extraction techniques which can provide a high accuracy rate of mass classification in the computer-aided lesion diagnosis of breast tumor. Totally 290 features were extracted using the newly developed border irregularity feature extractor as well as multiple sonographic features based on the breast imaging-reporting and data system (BI-RADS) lexicons. To demonstrate the performance of the proposed features, 4,107 ultrasound images containing 2,508 malignant cases were used. The clinical results demonstrate that the proposed feature combination can be an integral part of ultrasound CAD systems to help accurately distinguish benign from malignant tumors.
The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.
The X-ray mammography is the primary imaging modality for breast cancer screening. For the dense breast,
however, the mammogram is usually difficult to read due to tissue overlap problem caused by the superposition
of normal tissues. The digital breast tomosynthesis (DBT) that measures several low dose projections over a
limited angle range may be an alternative modality for breast imaging, since it allows the visualization of the
cross-sectional information of breast. The DBT, however, may suffer from the aliasing artifact and the severe noise
corruption. To overcome these problems, a total variation (TV) regularized statistical reconstruction algorithm
is presented. Inspired by the dual formulation of TV minimization in denoising and deblurring problems, we
derived a gradient-type algorithm based on statistical model of X-ray tomography. The objective function is
comprised of a data fidelity term derived from the statistical model and a TV regularization term. The gradient
of the objective function can be easily calculated using simple operations in terms of auxiliary variables. After
a descending step, the data fidelity term is renewed in each iteration. Since the proposed algorithm can be
implemented without sophisticated operations such as matrix inverse, it provides an efficient way to include the
TV regularization in the statistical reconstruction method, which results in a fast and robust estimation for low
dose projections over the limited angle range. Initial tests with an experimental DBT system confirmed our
Characterizing and locating sub-surface tumors will greatly enhance the detection and treatment of breast cancer.
In this paper, a novel tactile sensation imaging system, that is capable of detecting and characterizing the subsurface
object, was designed, implemented, and tested. A multi-layer Polydimethylsiloxane optical waveguide
has been fabricated as the sensing probe. The light was illuminated below the acceptance angle to totally
reflect within the flexible and transparent waveguide. When a waveguide is compressed by an external force,
the contact area of the waveguide deforms and causes the light to scatter. The scattered light is captured by a
high resolution camera and saved as an image. Using the salient features of the captured image, we estimated
inclusion characteristics such as size, depth, and Young's modulus. To test the performance of the proposed
system, we use a realistic tissue phantom with embedded stiff inclusions. The experimental results showed that
the proposed system can detect inclusions and provide the relative values of inclusion's mechanical properties.
Using these relative values, we can discern malignant and benign tumors.
Spectral X-ray imaging is a promising technique to drastically improve the diagnostic quality of radiography and
computed tomography (CT), since it enables material decomposition and/or identification based on the energy
dependency of material-specific X-ray attenuation. Unlike the
charge-integration based X-ray detectors, photon counting
X-ray detectors (PCXDs) can discriminate the energies of incident
X-ray photons and thereby multi-energy images can
be obtained in single exposure. However, the measured data are not accurate since the spectra of incident X-rays are
distorted according to the energy response function (ERF) of a PCXD. Thus ERF should be properly estimated in
advance for accurate spectral imaging. This paper presents a simple method for ERF estimation based on a
polychromatic X-ray source that is widely used for medical imaging. The method consists of three steps: source spectra
measurement, detector spectra reconstruction, and ERF inverse estimation. Real spectra of an X-ray tube are first
measured at all kVs by using an X-ray spectrometer. The corresponding detector spectra are obtained by threshold scans.
The ERF is then estimated by solving the inverse problem. Simulations are conducted to demonstrate the concept of the