Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert
pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of
the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and time-consuming
process with considerable variations in diagnosis between the experts. This paper presents a computer aided
decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The
main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and
repeatable manner. The paper proposes three different methods which presents and compares the results using 475
images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases.
This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation,
feature extraction, classification, grading and disease identification. Cervical histological images are captured using a
digital microscope. The images are captured in sufficient resolution to retain enough information for effective
classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous
epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of
segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the
background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image
features that influence the pathologists’ decision during the analysis and classification of a cervical biopsy are the
nuclei’s shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the
abnormalities. Similar features are extracted towards the automated classification process. This paper will present
various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative
metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the
classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results
of the grading process with five stages of the cancer spectrum.
A decision support system has been developed to assist the radiologist during mammogram classification. In this paper,
mass identification and segmentation methods are discussed in brief. Fuzzy region-growing techniques are applied to
effectively segment the tumour candidate from surrounding breast tissue. Boundary extraction is implemented using a
unit vector rotating about the mass core. The focus of this work is on the feature extraction and classification processes.
Important information relating to the malignancy of a mass may be derived from its morphological properties. Mass
shape and boundary roughness are primary features used in this research to discriminate between the two types of
lesions. A subset from thirteen shape descriptors is input to a binary decision tree classifier that provides a final diagnosis
of tumour malignancy. Features that combine to produce the most accurate result in distinguishing between malignant
and benign lesions include: spiculation index, zero crossings, boundary roughness index and area-to-perimeter ratio.
Using this method, a classification result of high sensitivity and specificity is achieved, with false-positive and falsenegative
rates of 9.3% and 0% respectively.
The first stage in the development of a clinically valid surgical simulator for training otologic surgeons in performing
cochlea implantation is presented. For this purpose, a geometric model of the temporal bone has been derived from a
cadaver specimen using the biomedical image processing software package Analyze (AnalyzeDirect, Inc) and its
three-dimensional reconstruction is examined. Simulator construction begins with registration and processing of a
Computer Tomography (CT) medical image sequence. Important anatomical structures of the middle and inner ear are
identified and segmented from each scan in a semi-automated threshold-based approach. Linear interpolation between
image slices produces a three-dimensional volume dataset: the geometrical model. Artefacts are effectively eliminated
using a semi-automatic seeded region-growing algorithm and unnecessary bony structures are removed. Once validated
by an Ear, Nose and Throat (ENT) specialist, the model may be imported into the Reachin Application Programming
Interface (API) (Reachin Technologies AB) for visual and haptic rendering associated with a virtual mastoidectomy.
Interaction with the model is realized with haptics interfacing, providing the user with accurate torque and force
feedback. Electrode array insertion into the cochlea will be introduced in the final stage of design.