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UCSB06 3
CMU SCS
Outline
• PART1: ViVo: Visual Vocabulary for cat retina images
• [PART2: other related work– FALCON: relevance feedback for image by
content– Drosophila embryo image mining
]
UCSB06 4
CMU SCS
PART1: ViVo
• with Ambuj Singh, Mark Verardo, Vebjorn Ljosa, Arnab Bhattacharya (UCSB)
• Jia-Yu Tim Pan, HJ Yang (CMU)
UCSB06 5
CMU SCS
Detachment Development
Normal1 day after detachment
3 days after detachment
7 days after detachment
28 days after detachment
3 months after detachment
UCSB06 6
CMU SCS
Data and Problem
• (Data) Retinal images taken from cats• (Problem) What happens in retina after
detachment?– What tissues (regions) are involved? – How do they change over time?
• How will a program convey this info?• More than classification
“we want to learn what classifier learned”
UCSB06 7
CMU SCS
Main idea
• extract characteristic visual ‘words’
• Equivalent to characteristic keywords, in a collection of text documents
UCSB06 8
CMU SCS
Visual Vocabulary (ViVo) generation
Step 1: Tile image
Step 2: Extract tile features
Step 3: ViVo
generation
Visualvocabulary
V1
V2
Feature 1
Fea
ture
2
8x12 tiles
UCSB06 10
CMU SCS
Evaluation of ViVo method
• how meaningful are the discovered ViVos?
• can they help in classification?
• generality?
• how else can they help biologists create hypotheses?
UCSB06 11
CMU SCS
Biological interpretationID ViVo Description Condition
V1 GFAP in inner retina (Müller cells) Healthy
V10 Healthy outer segments of rod photoreceptors
Healthy
V8 Redistribution of rod opsin into cell bodies of rod photoreceptors
Detached
V11 Co-occurring processes: Müller cell hypertrophy and rod opsin redistribution
Detached
UCSB06 12
CMU SCS
Goals:
• how meaningful are the discovered ViVos?
• can they help in classification?
• generality?
• how else can they help biologists create hypotheses?
UCSB06 13
CMU SCS
Quality of ViVo – by classification
N 1d 3d 7d 28d 28dr 6dO2 3m
N 7 2
1d 7
3d 12 1 1 1
7d 1 8 2
28d 1 23 2
28dr 1 21
6dO2 1 1 9
3m 5
Truth
Predicted
86% accuracy46 ViVos (90% energy)
UCSB06 14
CMU SCS
Goals:
• how meaningful are the discovered ViVos?
• can they help in classification?
• generality?
• how else can they help biologists create hypotheses?
UCSB06 16
CMU SCS
Protein images – MPEG7 CS
Giantin Hoechst LAMP2 NOP4 Tubulin
Giantin 30
Hoechst 30
LAMP2 50 9 1
NOP4 1 8 2
Tubulin 1 23
Truth
Predicted
84% accuracy4 ViVos (93% energy)1-NN classifier
UCSB06 17
CMU SCS
Evaluation of ViVo method
• how meaningful are the discovered ViVos?
• can they help in classification?
• generality?
• how else can they help biologists create hypotheses? ‘ViVo-annotation’!
UCSB06 18
CMU SCS
Automatic ViVo-annotation of images
• A tile represents a ViVo vk if the largest coefficient of the tile is along the kth basis vector
• A ViVo vk represents a class ci if the majority of its tiles are in that class
• For each image, the representative ViVos for the class are automatically highlighted
UCSB06 22
CMU SCS
Conclusions:
• how meaningful are the discovered ViVos?
• can they help in classification?
• generality?
• how else can they help biologists create hypotheses?
UCSB06 23
CMU SCS
Outcome/status
• ViVos: Automatic Visual Vocabulary generation for biomedical image mining, Bhattacharya, Ljosa, Pan, Verardo, Yang, Faloutsos, Singh; ICDM’05 (one of 5 best student paper award)
• Software – MATLAB code
• Tutorial in SIGMOD’05 (Murphy+Faloutsos)
UCSB06 24
CMU SCS
Outline
• PART1: ViVo: Visual Vocabulary for cat retina images
• PART2: FALCON: relevance feedback for image by content: SEE DEMO, later
• Ongoing work: Drosophila Fly Embryos
UCSB06 26
CMU SCS
Outline
• PART1: ViVo: Visual Vocabulary for cat retina images
• PART2: FALCON: relevance feedback for image by content: SEE DEMO, later
• Ongoing work: Drosophila Fly Embryos
UCSB06 29
CMU SCS
FEMine: Drosophila embryos
• Feature extraction
• ICA
• query by image content, mining, clustering
with Andre Balan, Eric Xing, Tim Pan