1. Image Processing (I) Fundamental units  2D – pixel  3D – voxel Orthogonal views: transverse (or axial), coronal, sagittal Image processing: preprocessing, segmentation, object representation and recognition, quantitative analysis coronal plane sagittal plane transverse plane

2. Image Processing (II) – Slices vs. Projections X Z Maximum Intensity Projection (MIP) Weighted-Sum Projection Y

3. Medical Imaging Objective: assist the physician in study/identification of anomaly in the organism Medicine domain knowledge + image processing + visualization Topological & geometrical analysis Validation issues – how to obtain ground truth? Need of human interaction Scanning techniques – CT, MR, PET, Ultrasound (US) CT: computed tomography MRI: magnetic resonance imaging PET: positron emission tomogrphay Resolution issue: delta z usually significantly larger than delta x and delta y

4. Bioinformatics Use information techniques to solve biological problem Reasons to exist  A great deal amount of biological data  Identification of known information  Discovery of hidden pattern  Quantification DNA -> mRNA -> amino acid -> protein A, G, C, T basics – base pair, pairing rule Exon, intron, gene PCR (Polymerase Chain Reaction)

5. Microarray Arraying Process Ordered sets (in 2D array) of DNA molecules (usually oligonucleotide or cDNA) attached to solid surface (glass, silicon, or nylon) Matrix is coated materials to be reactive; known DNA sequence segments (of genes) spotted on surface and hybridized with specimen’s RNA that are labeled (usually with fluorescent nucleotide) Spot intensities (or amount of fluorescence) correspond to transcript levels of particular gene 35 to hundreds of bp

6. Decision Trees Choose between options by projecting likely outcomes Draw a decision tree in terms alternate decisions or all possible outcomes Evaluate the decision tree

7. Decision Tree Example: Stick-Picking Game 4 3 2 2110 1000 0 12 1 2 12 12 1 1 1 The second player to pick the stick can always win!!  Two players  Each player takes turn to take off either 1 or 2 sticks at a time  The play taking the last stick(s) loses the game

8. K-Means Clustering Method A k-means algorithm is implemented in 4 steps:  Partition objects into k nonempty subsets  Compute seed points as the centroids of the clusters of the current partition. The centroid is the center (mean point) of the cluster.  Assign each object to the cluster with the nearest seed point.  Go back to Step 2, stop when no more new assignment.

9. Example: K-Means Clustering