Robert B. Vincelette

Empirical Analysis of Libs Images for Ovarian Cancer Detection

To develop a way to detect early epithelial ovarian cancer we expose blood samples to a laser. Using Laser induced breakdown spectroscopy, (LIBS) plasma images of the blood samples are generated and analyzed. In this paper we compare the images from blood specimens of cancer free mice to those of transgenic mice; that is, mice that are currently developing epithelial ovarian cancer and are approximately the same age as the disease free mice. Our ultimate goal is to look for changes in the shape and edges of the image that might indicate the presence of cancer. To analyze the images we use a nonlinear diffusion filter for enhancement of relevant image edges and removal of noise and irrelevant texture.

Algorithms for radar image identification and classification

We present and compare two different novel methods for classification of aircraft categories of Inverse Synthetic Aperture Radar (ISAR) images. The first method forms numerical equivalents to shape, size and other aircraft features as critical criteria to constitute the algorithm for their correct classification. The second method compares each ISAR image to unions of images of the different aircraft categories. We computer simulated five different categories of ISAR images and took two more from the internet. ISAR images are constructed based on the Doppler shifts of various parts, caused by the rotation of the aircraft and the radar reflection pulse shape which includes the size or duration of the radar pulse. The proposed classification algorithms were tested on these seven categories. All seven different aircraft models are flying a holding pattern. The aim of both algorithms is to quickly match and determine the similarity of the captured aircraft to the seven different categories where the aircraft is in any position of a prescribed holding pattern. Our experimental results clearly indicate that in most parts of the holding pattern the category of the aircraft can be successfully identified with both proposed methods. The union method shows more successful identification results and is superior to the results we obtained in the first proposed method.

Aircraft identification by unions of ISAR images

We offer an algorithm that can identify aircraft categories from Inverse Synthetic Aperture Radar (ISAR) images that use both the radar reflection pulse shape, which includes the duration or size of the radar pulse that is reflected, and the Doppler shifts of different parts of the aircraft caused by rotational motions of the aircraft as it maneuvers. We investigated the practicality of determining which of seven different aircraft categories a radar return indicates. The object of this research is to very quickly tell from an ISAR return how an aircraft compares to the seven different categories where the aircraft is in any position of a prescribed holding pattern. We propose a new method in which we compare each ISAR image to unions of images of the different aircraft categories. This method gave us results that are superior to the results we obtained in [8].

Efficient shape recognition method using novel metric for complex polygonal shapes

We propose two new optimal and sub-optimal solution shape recognition algorithms using geometric calculations. Our algorithms are efficient and tolerate severe noise. They work for convex and concave polygons equally well. These algorithms are invariant under translation, rotation, change of scale and they are reasonably easy to compute.

Noise resistant algorithm for radar images recognition and classification

We propose a novel algorithm for automatic aircraft classification. The proposed method makes numerical equivalents to shape, size and other aircraft features as critical criteria to constitute the algorithm for their correct classification. This method uses Inverse Synthetic Aperture Radar (ISAR) aircraft images that are making maneuvers that introduce aircraft rotation relative to the radar station. By means of analyzing the shape of the radar pulse and Doppler shifts that are caused by rotation of the aircraft, an image of the aircraft shape can be constructed. We computer simulated five different categories of ISAR images. We tested the proposed classification algorithm on these five categories and on two more categories taken from the Internet. One aircraft model is simulated and the other one is a real sequence with much added noise. All seven different aircraft models are flying a holding pattern. We investigated where in the holding patterns ISAR reflections made it possible to identify each category of aircraft. Our experimental results demonstrate that in most parts of the holding pattern the category of the aircraft can be successfully identified. The performed tests show that the proposed algorithm appears to be noise resistant.

Combined nonlinear inverse diffusion filter and triangle method used for noise removal from polygonal shapes

A two step procedure for removing noise from polygonal shapes is presented here. The first step is the removal of vertices by linear and different nonlinear inverse diffusion filters. In the second step we apply the triangle method which identifies least dominant vertices from the polygon obtained from the first stage. Those vertices whose adjacent sides form triangles of the smallest area are defined as least dominant and most likely to be noise. A thorough testing of this method on the shapes typical in video images demonstrates that it successfully removes noise vertices while it preserves dominant ones.

Noise removal from polygonal shapes using combined inverse diffusion filter and triangle method

We introduce a method for de-noising, segmenting and measuring similarity of shapes. The proposed method consists of two techniques. First we use an inverse diffusion filter for enhancement of relevant polygonal vertices and removal of noise and irrelevant edges. Second we apply a technique that removes the vertices that form the triangles with the smallest areas. In a thorough experimental evaluation, the combined method shows successful noise suppression while preserving dominant vertices present in the input shape.

Empirical Analysis of Blood Plasma LIBS Images Using the Nonlinear Diffusion Method

In an attempt to develop a method of early epithelial ovarian cancer detection we apply a laser to blood samples. With laser induced breakdown spectroscopy, (LIBS), plasma state images of blood samples are prepared and analyzed. We apply an improved nonlinear diffusion filter in order to enhance relevant image edges and to remove noise and irrelevant texture. We hope to identify differences in the shapes of these images by which we can detect the earliest stages of ovarian cancer.

Empirical analysis of LIBS images using the nonlinear diffusion method

We are investigating the use of the laser induced breakdown spectroscopy, (LIBS), on blood samples of mice to detect the earliest stages of epithelial ovarian cancer (EOC). A laser changes a blood samples to plasma state and the images produced thereby are analyzed. By comparing LIBS images of blood from EOC positive mice to those of cancer free mice, our goal is to identify differences by which we can detect those in early EOC stages. We apply an improved nonlinear diffusion filter to enhance relevant image edges and to remove noise and irrelevant texture.