Alina Sultana

Detection of pectoral muscle in mammograms using a mean-shift segmentation approach

Detection of pectoral muscle in mammograms is an important pre-processing segmentation step. The pectoral muscle is one of the few anatomical features that appears clearly and reliably in medio-lateral oblique view mammograms. This new method overcomes the limitation of the straight-line representation considered in our initial investigation using the Hough transform.

Analysis of Pap smear images with iso- and edge-contours

A novel method for the detection and segmentation of nuclei and cells in Pap smear images is introduced. The method is based on a geometric analysis of iso- and edge-contours. For nuclei detection we employ isocontours taken at different levels of intensity and we report best detection (object) recall values as well as best segmentation precision values. For cell outline detection, we employ traditional edge-based contours and show that with a simple radial analysis one can detect the outline even in agglomerated cells - which so far has been approached rather hesitantly. The system was tested on three different databases.

A new approach in breast image registration

According to the World Health Organization, breast cancer is the most common cancer suffered by women in the world, which during the last two decades, has increased the women mortality in developing countries. Mammography is the best method used for the screening; the problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and, on the other hand, to the poorly defined border between the healthy and the cancerous zone. An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. Using this technique, the breast region is firstly identified by using an adaptive thresholding algorithm. The focus is on determining control points in the two mamograms; these points are used to put the two mammograms into correspondence. The algorithms performance was evaluated on a large number of difficult cases and found to be adequate.

Non-linear parametric derivation of contour detectors for cellular images

This paper proposes an extension of the widely used Logarithmic Image Processing (LIP) models by means of parametrization. The mathematical structure of the mentioned models is that of cone or vector space. Once the mathematical investigation defined the boundaries of these structures, parametric extensions of the known models are straight-forward. It has been showed that the implementation of Laplacian edge detector techniques under the LIP model yields superior performance. In this paper we shall prove that parametrization not only adds flexibility, but may also lead to superior quality.

Automatic pap smear nuclei detection using mean-shift and region growing

The Babes-Papanicolaou test (also known as Pap smear) is a method of cervical cancer screening used to detect abnormal cells which are or can become cancerous. Since the visual inspection of pap smears is very time consuming, the need for automatic methods is required. This paper presents an algorithm for the automatic detection of nuclei within pap smears images. The algorithm relies in the highly effective mean-shift filtering method which enhances the contrast of nuclei areas. The segmentation consists of a region growing with starting points taken from the image gradient map. Size and eccentricity measures are used to keep only nuclei from the segmented regions. The method is validated on two different pap smear test databases and the detection rate is above 91%.

Automatic segmentation of infantile hemangiomas

Infantile hemangiomas are the most common types of tumors that are found in infants and have an incidence of approximately 10% in the common population. Although most infantile hemangiomas are self-involuting, due to their fast proliferation they may threaten vital anatomical structures and physiological functions; also, the involution process may take up to several years. An accurate monitoring of the progress of hemangioma growth and regression is essential. We thus suggest using a computer aided follow-up monitoring of these lesions by an automatic detection and quantifying the lesion dynamic: regression or proliferation. In this study we propose some image enhancements methods and also a preliminary color based segmentation. We have tested our methods on 25 hemangioma cases and compared the automated segmentation results with clinician-determined segmentation using an area percentage error.

A new contour extraction algorithm for ToF images

This paper presents a new contour extraction algorithm for time-of-flight (ToF) camera distance images. Experimental results and comparisons with the classical grey level contour extractors are presented. A mathematical model for contour validation is developed, and finally, an edge alignment method is proposed.

Automatic monitoring system for the detection and evaluation of the evolution of hemangiomas

In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Fuzzy C-means. Using the area and the redness of the hemagiomas extracted with Fuzzy C-means, for the same patient, at different moments of time, the algorithm decides whether the hemangioma is evolving, stationary or regressing. Because it is also useful to understand how the tumors shape is changing in time, we have also included a method of matching and overlapping the hemangioma regions.

Automatic evaluation of hemangiomas for follow-up monitoring

Infantile hemangiomas are the most common types of tumors with an incidence of approximately 10% in the common population. An accurate monitoring of the progress of hemangioma growth and regression is essential for an effective treatment. This study presents an automatic evaluation of the evolution of hemangioma on a follow-up series of images based on color and area features. A color constancy approach is applied to correct the variation of ambient illumination. The hemangioma segmentation uses a two-level thresholding approach with some post-processing methods. The proposed method has been tested on 25 hemangioma cases annotated by clinicians.

Detection of mammographic microcalcifications using a refined region-growing approach

Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and, on the other hand, to the poorly defined border between the healthy and the cancerous zone. Even though it has been studied for many years, there are still remaining challenges and directions for future research, such as developing better enhancement and segmentation algorithms. In this paper, we propose a microcalcification detection algorithm using an extended region growing technique in a mammography CAD (computer-aided diagnosis) system.