The document highlights various medical imaging projects developed using MATLAB, focusing on techniques for cell segmentation, ultrasound image processing, and cancer detection. Each project addresses specific challenges in medical imaging, such as misclassification errors and noise reduction, with evaluations based on accuracy and effectiveness. These projects may enhance the automation and accuracy of diagnostics in medical fields, including cancer and leukemia detection.

1. M.E Computer Science Medical Imaging Projects
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List Link : http://kasanpro.com/projects-list/m-e-computer-science-medical-imaging-projects
Title :Evaluation of Segmentation Algorithms on Cell Populations Using CDF Curves
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/evaluation-segmentation-algorithms-cell-populations-using-cdf-curves
Abstract : Cell segmentation is a critical step in the analysis pipeline for most imaging cytometry experiments and
evaluating the performance of segmentation algorithms is important for aiding the selection of segmentation
algorithms. Four popular algorithms are evaluated based on their cell segmentation performance. Because
segmentation involves the classification of pixels belonging to regions within the cell or belonging to background,
these algorithms are evaluated based on their total misclassification error. Misclassification error is particularly
relevant in the analysis of quantitative descriptors of cell morphology involving pixel counts, such as projected area,
aspect ratio and diameter. Since the cumulative distribution function captures completely the stochastic properties of
a population of misclassification errors it is used to compare segmentation performance.
Title :Automatic Segmentation and Feature Extraction of Uterine Fibroid using Ultrasound Images
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/automatic-segmentation-feature-extraction-uterine-fibroid-ultrasound-images
Abstract : Uterine fibroid is the most common benign (non cancerous) tumor of the female in the world. The most
crucial factor is presence of fibroid can cause infertility and repeated miscarriage. An ultrasound image of fibroids
requires image segmentation, but the quality of ultrasound image is limited by speckle noise. This makes it difficult to
segment the ultrasound images. In this paper the image is preprocessed to remove speckle noise by Discrete
Wavelet Transform (DWT). The preprocessed images respond well to local Phase- Based Level set method to
segment the fibroid. The proposed approach performances are evaluated in terms of accuracy, specificity and
sensitivity.
Title :Clustering Performance Analysis of FCM Algorithm on Iterative Relaxed Median Filtered Medical Images
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/fcm-algorithm-iterative-relaxed-median-filtered-medical-images
Abstract : Noise removal is a major concern in image processing particularly in medical imaging. In this paper, a
novel noise removal technique called Iterative relaxed median tilter (lRMF) has been proposed and the effect of noise
removal, by means of median filtering, on Fuzzy C-Means Clustering (FCM) has been analysed. Noise removal is
carried out by various median filtering methods such as standard median filter (SMF), adaptive median tilter (AMF),
hybrid median filter (HMF) & relaxed median filter (RMF) and the performance of these methods is compared with the
proposed method.
Title :MRF-Based Deformable Registration and Ventilation Estimation of Lung CT
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/mrf-based-deformable-registration-ventilation-estimation-lung-ct
Abstract : Deformable image registration is an important tool in medical image analysis. In the case of lung computed
tomography (CT) registration there are three major challenges: large motion of small features, sliding motions
between organs, and changing image contrast due to compression.Recently,Markov random field (MRF)-based
discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for
registration, in particular its susceptibility to local minima. However, to date the simplifications made to obtain tractable

2. computational complexity reduced the registration accuracy.We address these challenges and preserve the
potentially higher quality of discrete approaches with three novel contributions. First, we use an image-derived
minimum spanning tree as a simplified graph structure, which copes well with the complex sliding motion and allows
us to find the global optimum very efficiently. Second, a stochastic sampling approach for the similarity cost between
images is introduced within a symmetric, diffeomorphic B-spline transformation model with diffusion regularization.
The complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces. In
addition to the geometric transform labels, hyper-labels are introduced, which represent local intensity variations in
this task, and allow for the direct estimation of lung ventilation.We validate the improvements in accuracy and
performance on exhale-inhale CT volume pairs using a large number of expert landmarks.
Title :Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/cancer-detection-classification
Abstract : Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer.
Pathologic information obtained from the prostatectomy specimen provides important prognostic information and
guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily
qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection
and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges
of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system
uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a
superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer
versus noncancer, and low-grade versus high-grade cancer.We evaluated our system using 991 sub-images
extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We
measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade
classification tasks, respectively. This system represents a first step toward automated cancer quantification on
prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy
patient care.
M.E Computer Science Medical Imaging Projects
Title :Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/acute-myelogenous-leukemia-detection-blood-microscopic-images
Abstract : Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults. The
average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current
methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming,
and its accuracy depends on the operator's ability. In this paper, a simple technique that automatically detects and
segments AML in blood smears is presented. The proposed method differs from others in: 1) the simplicity of the
developed approach; 2) classification of complete blood smear images as opposed to subimages; and 3) use of these
algorithms to segment and detect nucleated cells. Computer simulation involved the following tests: comparing the
impact of Hausdorff dimension on the system before and after the influence of local binary pattern, comparing the
performance of the proposed algorithms on subimages and whole images, and comparing the results of some of the
existing systems with the proposed system. Eighty microscopic blood images were tested, and the proposed
framework managed to obtain 98% accuracy for the localization of the lymphoblast cells and to separate it from the
subimages and complete images.
Title :Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and
Support Vector Machines
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/cervical-cancer-image-classification-using-texture-analysis-svm
Abstract : Dynamic Contrast Enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue.
Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The
aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer
could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy
DCE-MRI. First order statistical features of the Brix parameters, such as mean, variance and percentiles, were used.
In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices
(GLCM) from the maps, resulting in second order statistical features that captured spatial variations within the tumors.
Clinical factors and first and second order features were used as explanatory variables for support vector machine

3. (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out
cross-model validation, which is stricter than the common leave-one-out cross-validation. In addition, a random value
permutation test was used to evaluate model statistical significance. Features derived from first order statistics could
not discriminate between cured and relapsed patients (specificity 0-20%, p-values close to unity). However, second
order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical
factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by
texture features linked to tumor heterogeneity, were more suitable for outcome prediction than first order statistical
features.