Marek Kowal

Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.

Neuro-fuzzy networks and their application to fault detection of dynamical systems

The paper tackles the problem of robust fault detection using Takagi-Sugeno neuro-fuzzy (N-F) models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such an approach is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection, the adaptive threshold technique is used to deal with the problem. The paper focuses also on the N-F model design procedure. The bounded-error approach is applied to generate rules for the model using available data. The proposed algorithms are applied to fault detection in a valve that is a part of the technical installation at the Lublin sugar factory in Poland. Experimental results are presented in the final part of the paper to confirm the effectiveness of the method.

Nuclei segmentation for computer-aided diagnosis of breast cancer

Abstract Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.

Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation

The paper presents k-means based hybrid segmentation method for breast cancer diagnosis problem. It is part of the computer system to support diagnosis based on microscope images of the fine needle biopsy. The system assumes distinguishing malignant from benign cases. Described method is an alternative to the previously presented algorithms based on fuzzy c-means clustering and competitive neural networks. However, it uses similar idea of combining clustering in RGB space with adaptive thresholding. At first, thresholding reveals objects on background. Then image is clustered with k-means algorithm to distinguish nuclei from red blood cells and other objects. Correct segmentation is crucial to obtain good quality features measurements and consequently successful diagnosis. The system of malignancy classification was tested on a set of real case medical images with promising results.

Fuzzy Clustering and Adaptive Thresholding Based Segmentation Method for Breast Cancer Diagnosis

The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. The approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing malignant from benign cases. Research is focused on two different problems. The first is segmentation and extraction of morphometric parameters of nuclei present on cytological images. The second concentrates on breast cancer classification using selected features. Studies in both areas are conducted in parallel. This work is mainly devoted to the problem of image segmentation in order to obtain good quality features measurements. Correct segmentation is crucial for successful diagnosis. The paper describes hybrid segmentation algorithm based on fuzzy clustering and adaptive thresholding. The automatic system of malignancy classification was applied on a set of medical images with promising results.

Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering

This document presents the stage of research concerning an automatic diagnosis system of breast cancer based on cytological images of FNB (Fine Needle Biopsy). The work concentrates on the image segmentation phase, which is employed to find nucleus in cytological images. The accuracy and correctness of the image segmentation algorithm is a critical factor for successful diagnosis due to the fact that case classification is based on morphometrical features extracted form segmented nucleus. The presented approach to image nucleus segmentation is based on the FCMS (Fuzzy C-Means with Shape function) clustering algorithm. Traditional approaches to image segmentation using clustering algorithms consider clustering pixels in color space in order to recognize objects. The novelty of the presented approach is that the clustering process is conducted in color space but the searched objects must have an arbitrarily defined shape. Simulations and experimental results are included in the work to illustrate the effectiveness of the proposed approach.

Fault Detection Under Fuzzy Model Uncertainty

The paper tackle a problem of robust fault detection using Takagi-Sugeno fuzzy models. The model-based strategy is employed to generate the residuals in order to make decision about the state of the process. Unfortunately, this method is corrupted by the model uncertainty due to the fact that in real applications model-reality mismatch usually exist. In order to ensure the reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model structure design. The bounded error approach is applied to generate the rules for the model using available measurements. Proposed approach is applied to fault detection in the DC laboratory engine.

Robust Fault Detection Using Neuro-fuzzy Networks

Abstract The paper focuses on the problem of robust fault detection using neuro-fuzzy model based strategies. The main objective of the work is to show how to employ bounding error approach to determine the uncertainty of the neuro-fuzzy model and next utilize this knowledge for robust fault detection. The paper presents also how to tackle the problem of choosing the right structure of the neuro-fuzzy models. Proposed algorithms are applied to fault detection in the valve that is the part of the technical installation at the Lublin sugar factory. Experimental results presented in the final part of the paper confirms the effectiveness of the proposed methods.

Swarm Intelligence Algorithms for Multi-level Image Thresholding

The multi-level thresholding is one of the most important issues in image segmentation. It is a time consuming problem, i.e. finding appropriate threshold values could take exceptionally long computational time. In this paper we evaluate and compare three meta-heuristic techniques tackling to this problem: ant colony, fireflies and honey bee mating. The proposed methods are validated by illustrative examples. The results show that the investigated methods do not only provide good segmentation results but also their computational effort makes them very efficient approaches.

NEURO-FUZZY STRUCTURES IN FDI SYSTEM

Abstract Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy (N-F) model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the actuators of one sugar factory.