1.1 - Optical flow.
1.2 - Boundary Detection by Dynamic Programming.
1.4 - Characterizing arteriosclerosis by texture analysis.
1.6 - Cell segmentation.
1.7 - Cells classification.
1.8 - Mitosis detection in breast histological images.
4.5 - Green balls.
5.1.1 - Skin cancer - segmentation of melanoma images.
5.1.2 - Skin cancer - feature extraction for ABCDE system.
5.1.3 - Skin cancer - Computer Aided Diagnosis system.
5.2.1 - Oral cancer - lesion segmentation.
5.2.2 - Oral cancer - classification of precancerous lesions.
5.3 - Mammography image database.
5.4 - Shading Correction of Retinal Images.
5.5 - DTI diffusion tensor imaging -MRI.
5.6 - Lung nodule detection in CT.
5.7 - Diabetic retinopathy.
5.8 Bilateral filtering of fMRI data.
5.9 MR Brain Image Segmentation.
5.10 Electromagnetics Breast Model on MR Images.
6.1.1 - SIFT - traffic sign detection.
6.1.2 - SIFT - banknote authentication.
6.2 - Waterline detection using livewire.
6.3 - Active Shape Models.
6.5 - Image segmentation using PCNN.
6.6 - Image segmentation using Reinforcement Learning.
6.8 - Liver segmentation from CT datasets.
6.9 - Optical Character Recognition.
7.1 - Image Enhancement for Object Detection.
8.1 - Automatic Analysis of Pap Smears.
X.1 - Your own project. 
by artur.chodorowski@chalmers.se