Eran Gur

Cell nuclei segmentation using fuzzy logic engine

The task of segmenting cell nuclei in microscope images is a classical image analysis problem. The accurate nuclei segmentation may contribute to development of successful system which automate the analysis of microscope images for pathology detection. In this article we describe a method for semi-supervised training of fuzzy logic engine. The fuzzy logic engine is applied to connect a set of parameters proven to be important for nucleus segmentation. In addition each parameter for itself is detected using a set of fuzzy logic rules. We present results of nuclei segmentation using fuzzy logic set of rules.

Toward fast malaria detection by secondary speckle sensing microscopy

Diagnosis of malaria must be rapid, accurate, simple to use, portable and low cost, as suggested by the World Health Organization (WHO). Despite recent efforts, the gold standard remains the light microscopy of a stained blood film. This method can detect low parasitemia and identify different species of Plasmodium. However, it is time consuming, it requires well trained microscopist and good instrumentation to minimize misinterpretation, thus the costs are considerable. Moreover, the equipment cannot be easily transported and installed. In this paper we propose a new technique named “secondary speckle sensing microscopy” (S3M) based upon extraction of correlation based statistics of speckle patterns generated while illuminating red blood cells with a laser and inspecting them under a microscope. Then, using fuzzy logic ruling and principle component analysis, good quality of separation between healthy and infected red blood cells was demonstrated in preliminary experiments. The proposed technique can be used for automated high rate detection of malaria infected red blood cells.

Image deblurring through static or time-varying random perturbation medium

In a large plurality of applications, imaging quality is significantly reduced due to existence of static or time-varying random perturbation media. An example of such a medium can be a diffusive window through which we wish to image an object located behind, and not in proximity to, the window. Another example can be localized flow of turbulence (above hot surfaces such as black roads) or of aerosols distorting the imaging resolution of objects positioned behind the perturbation. We present a new deblurring approach for obtaining highly resolved imaging of objects positioned behind static or time-varying random perturbation media. The proposed approach for extraction of the high spatial frequencies is based on iterative computation similar to the well-known Gerchberg-Saxton algorithm for phase retrieval. By focusing our camera onto three planes positioned between the imaging camera and the perturbation, we are able to retrieve the phase distribution of those planes and then reconstruct the intensity of the object by numerical free space propagation of this extracted complex field to the estimated position of the object.

Optical implementation of fuzzy-logic controllers. Part I

In recent years fuzzy-logic-based controllers have become the preferred tool for implementing control mechanisms in high-complexity systems. We suggest novel implementations of a basic fuzzy controller. First, we show the principles of fuzzy-logic control and explain the concept behind the optical controller. Next we review a one-dimensional fuzzy-logic-based optoelectronic controller that was suggested in 1994. Finally, we present a novel two-dimensional optical control approach accompanied by computer simulations of an inverted pendulum. The two-dimensional setup is first given for a dual-input controller and then expanded to a multi-input controller.

Retrieval of Rashi Semi-cursive Handwriting via Fuzzy Logic

Text recognition and retrieval is a well known problem. Automated optical character recognition (OCR) tools do not supply a complete solution and in most cases human inspection is required. In this paper the authors suggest a novel text recognition algorithm based on usage of fuzzy logic rules relying on statistical data of the analyzed font. The new approach combines letter statistics and correlation coefficients in a set of fuzzy based rules, enabling the recognition of distorted letters that may not be retrieved otherwise. The authors focused on Rashi fonts associated with commentaries of the Bible that are actually handwritten calligraphy.

Optical through-turbulence imaging configuration: experimental validation

In this Letter the authors present a field experimental validation of an imaging system that, when combined with a special image-processing algorithm, allows obtaining an improved imaging quality through turbulence perturbation. The system includes simultaneous capturing of three differently focused images and performing an iterative Gerchberg-Saxton based processing for phase retrieval. After the phase is retrieved the intensity of the object is reconstructed by numerical free-space propagation of the extracted complex field to the estimated position of the object.

Discussion on multidimensional fuzzy control

Fuzzy-logic inference engines are in use in various disciplines such as control systems, medicine, and the like. The use of optical tools to implement such engines may improve the performance and the flexibility of inference procedures. The optical processor works in a two-dimensional environment, whereas the inference engine might have to handle more than two independent input channels. Here several approaches to generating the first, to our knowledge, N-dimensional optical fuzzy processor are addressed. The first approach uses space multiplexing, the second approach uses polarization multiplexing, and the third approach uses wavelength multiplexing to increase the dimension of the processor.

Radon-Transform-Based Image Enhancement for Microelectronic Chip Inspection

In this paper, we present a new numerical approach for enhancing the resolving power of low-resolution (LR) images, which can be applied for failure analysis of microelectronic chips. The resolution improvement is based upon a numerical iterative comparison between a Radon transform of a high-resolution layout image and a Radon transform of an LR experimentally captured image of the same region of interest.

Superresolved Imaging of Microelectronic Devices for Improved Failure Analysis

In this paper, we present a new numerical approach for improving the resolving power of low-resolution (LR) images. This approach may be applied for failure analysis of microelectronic chips. The resolution improvement is based upon numerical iterative comparison between a LR experimentally captured image and a high-resolution layout image of the same region of interest.

Optical generation of fuzzy-based rules

In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.