This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in
medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for
denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara
filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also
proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged
images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells
(RBCs) into two sub-images based on the RBC (blood's dominant particle) size estimation is a critical step. Using
Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism
which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an
accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed
algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,
experiments were conducted on normal blood smear images. This framework was compared to other published
approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better
Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis
are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer
diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides
and its influence on malignancy classification. Classification of malignancy plays a very important role during
the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable
information about the cancer malignancy grade which helps to choose an appropriate treatment. This process
involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important.
In this work we compare three powerful segmentation approaches and test their impact on the classification of
breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation
and textural segmentation based on co-occurrence matrix.
Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes
four different classifiers were trained and tested with previously extracted features. The compared classifiers
are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network
(PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the
best results over the three compared approaches and leads to a good feature extraction with a lowest average
error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron
with an error rate of 3.07% using fuzzy c-means segmentation.
A computer aided root lesion detection method for digital dental X-rays is proposed using level set and complex wavelets. The detection method consists of two stages: preprocessing and root lesion detection. During preprocessing, a level set segmentation is applied to separate the teeth from the background. Tailored for the dental clinical environment, a segmentation clinical acceleration scheme is applied by using a support vector machine (SVM) classifier and individual principal component analysis (PCA) to provide an initial contour. Then, based on the segmentation result, root lesion detection is performed. Firstly, the teeth are isolated by the average intensity profile. Secondly, a center-line zero crossing based candidate generation is applied to generate the possible root lesion areas. Thirdly, the Dual-Tree Complex Wavelets Transform (DT-CWT) is used to further remove false positives. Lastly when the root lesion is detected, the area of root lesion is automatically marked with color indication representing different levels of seriousness. 150 real dental X-rays with various degrees of root lesions are used to test the proposed method. The results were validated by the dentist. Experimental results show that the proposed method is able to successfully detect the root lesion and provide visual assistance to the dentist.
A level-set-based segmentation framework for Computer Aided Dental X-rays Analysis (CADXA) is proposed. In this framework, we first employ level set methods to segment the dental X-ray image into three regions: Normal Region (NR), Potential Abnormal Region (PAR), Abnormal and Background Region (ABR). The segmentation results are then used to build uncertainty maps based on a proposed uncertainty measurement method and an analysis scheme is applied. The level set segmentation method consists of two stages: a training stage and
a segmentation stage. During the training stage, manually chosen representative images are segmented using hierarchical level set region detection. The segmentation results are used to train a support vector machine (SVM) classifier. During the segmentation stage, a dental X-ray image is first classified by the trained SVM.
The classifier provides an initial contour which is close to the correct boundary for the coupled level set method which is then used to further segment the image. Different dental X-ray images are used to test the framework. Experimental results show that the proposed framework achieves faster level set segmentation and provides more
detailed information and indications of possible problems to the dentist. To our best knowledge, this is one of the first results on CADXA using level set methods.
A method for reconstruction of 3D object models from multiple views of range image is proposed. It is very important to use these partially redundant data effectively to get an integrated, complete and accurate object model. The object shape is unconstrained, curved surfaces are allowed. From each view of range image, surfaces are segmented and fitted into planar and quadratic patches by a robust residual analysis method (we address this method in another paper). Analyzing the errors of fitted surfaces from each view, the final expressions of the surfaces are merged from every view. A boundary representative model (B-rep) is used to express the final complete object. The method can be used to create 3D models for object recognition.
In this paper, a new formulation and method is presented to directly recover 3D short term motion from range image sequences. In the case of a rigid-body motion, the formulation relates through a set of linear equations the six motion parameters to the first spatial-temporal derivatives and coordinates of a point. A weighted least square method is used to find the solution of this equation set. In case of locally rigid motion, the six rigid motion parameters of each point are estimated from the first and second spatial-temporal derivatives. For each point, a set of 10 linear equations with six unknowns is again solved by the least square method. The special case of local translation with small rotation gives a very elegant closed-form solution and an explicit geometric explanation. We also shown that the formulation can be easily generalized to any arbitrary motion. The proposed formulation has theoretical elegance since it only involves solving a set of linear equations. Results on both synthetic and real data are given.
This paper presents a robust segmentation and fitting technique. The method randomly samples appropriate range image points and fits them into selected primitive type. From K samples we measure residual consensus to choose one set of sample points which determines an equation to have the best fit for a homogeneous patch in the current processing region. A method with compressed histogram is used to measure and compare residuals on various noise levels. The method segments range image into quadratic surfaces, and works very well even in smoothly connected regions.