Introduction to Biomedical Image Analysis BMI 705 Winter 2009 Kun Huang Department of Biomedical Informatics Ohio State University

-Introduction to biomedical imaging -Imaging modalities -Components of an imaging system -Elements of image processing techniques -Machine learning and image analysis

-Why imaging? -Diagnosis X-ray, MRI, Ultrasound, microscopic imaging (pathology and histology) … -Functional analysis Functional MRI -Visualization (invasive and noninvasive) 3-D, 4-D -Phenotyping/Quantification Microscopic imaging for different genotypes, tissue microarray, cell count, volume rendering, Ca 2+ concentration …

-Why imaging? -Visualization (invasive and noninvasive) 3-D, 4-D

-Why imaging? -Phenotyping/Quantification Neuhaus’98 Calcium imaging (fluorescence) Invitrogen

-Why imaging? -Phenotyping/Quantification Perlman et. al., Science Nov ~600,000 images, ~70,000,000 cells, ~10 9 data points

-Why imaging? -Phenotyping/Quantification

Structural Complexity

Multiscale Multimodal Approach The blind men and the elephant Individual imaging modality can only probe one aspect of the system. A comprehensive understanding calls for the integration of multiple modalities. Function  Physiology  Tissue  Cell  Molecular  Dynamics Meter to nanometer

Dataset Size: Systems Biology Future big science animal experiments on cancer, heart disease, pathogen host response Basic small mouse is 3 cm 3 1 μ resolution – very roughly bytes/mouse Molecular data (spatial location) multiply by 10 2 Vary genetic composition, environmental manipulation, systematic mechanisms for varying genetic expression; multiply by 10 3 Total: bytes per big science animal experiment

-How to do imaging? -Interdisciplinary research -Electrical engineering -Physics -Chemistry -Mathematics -Computer science -Statistics -… -Biomedical sciences -Pathology -Radiology -Pharmacology -Clinical study -Patient care -…

-Components of Imaging System -Instrumentation : -Electrical engineering, physics, histochemistry … -Image generation -Sensor technology (e.g., scanner), coloring agents … -Image processing and enhancement -Both software, hardware, or experimental (dynamic contrast) -Image analysis at all levels -Image processing, computer vision, machine learning, pattern recognition, knowledge computing … -Image storage and retrieval -Database/data warehouse

-Components of Imaging System -Instrumentation : From Dr. Petra Schmalbrock Philips 7T

-Components of Imaging System -Instrumentation/image generation : Dr. Raman

-Areas of Image Processing and Analysis -Image enhancement -Color correction, noise removal, contrast enhancement … -Feature extraction -color, point, edge (line, curves), area -cell, tissue type, organ, region -Segmentation -Registration -3-D reconstruction -Visualization -Quantization

Medical Imaging vs. Biological Imaging Medical imaging is for clinical use. It is to implemented to facilitate human decision (diagnosis). E.g., Computer Aided Diagnosis (CAD). It will never replace human being in decision making. Mostly in vivo imaging. Imaging informatics is an important component of medical informatics. The storage, retrieval and processing of the image involve many legal and policy related issues as well as economic concerns. There are existing standards and commercial systems in storing and formatting the images. Real application requires long term validation and FDA application (including algorithms).

Medical Imaging vs. Biological Imaging Biological imaging is for scientific discovery. Computer is used to replace human in performing tedious quantitative tasks. Algorithms are usually highly domain specific. Many projects are related to microscopic imaging. Small animal in vivo imaging is also emerging. Large amount of data is a big issue.

DICOM Image Standard Digital imaging and communication in medicine Groups information into a single data file (set) Contains information such as patient ID, acquisiton parameters and conditions Consists of a header with both standard and freeform fields and image data

In Vivo Imaging vs. In Vitro Imaging In vivo imaging Live sample (not always live animal) X-ray Computer Tomography (CT) Magnetic Resonance Imaging (MRI) Electron Paramagnetic Resonance (EPR) Positron Emission Tomography (PET) Ultrasound Bioluminescence Imaging Optical Coherent Tomography Microscopy Video microscopy …

In Vivo Imaging vs. In Vitro Imaging In vivo imaging (cont’d) Live sample (not always live animal) Good for longitudinal study Resolution of many modalities are low Multimodalities are usually combined

In Vivo Imaging vs. In Vitro Imaging In vitro imaging Mostly microcopy imaging Light microscope Fluorescent microscope Multiple photo microscope Confocal microscope Multispectral microscope Atomic force microscope Electron microscope Video microscopy … Large data size is an issue

-Introduction to biomedical imaging -Imaging modalities -Components of an imaging system -Elements of image processing techniques -Machine learning and image analysis

Digital Image

imss(:,:,1) = imss(:,:,2) = imss(:,:,3) =

Digital Image

Simplest segmentation: thresholding

-Image Enhancement -Denoise -Averaging -Median filter 1/

Wavelet-based denoising

-Example Crystal detection Median filter removes “spikes” in the image. From M. Lee

Example -Crystal detection

-Image Enhancement -Color and intensity adjustment -Histogram equalization

-Image Enhancement -Color space transform RGB -> HSV, HSL, YCbCr, … R = 64 G = 31 B = 62 R = 125 G = 80 B = 147 H = 199 S = 117 V = 147 H = 214 S = 132 V = 64

More Sophisticated Operations -Color space transform RGB -> HSV, HSL, YCbCr, Lab, …

RGB  Lab K-means algorithm clusters the pixels in the new color space into three groups. Group merging. Mophorlogical operations.

-Feature Extraction -Region detection – morphology manipulation -Dilate and Erode -Open -Erode  dilate -Small objects are removed -Close -Dilate  Erode -Holes are closed -Skeleton and perimeter

− Example Cell detection

-Feature Extraction -Edge detection -Gradients -Canny edge detector -Gaussian smoothing -Gradients -Two thresholds -Thinning

-Feature Extraction -Edge detection

−Boundary detection and measurement −Active contour

−Boundary detection and measurement −Active contour

−Tools −PhotoShop −IrfanView −PaintShop −Metamorph −ImageJ −Matlab

-Example -Virtual Simulation of Temporal Bone Dissection