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7/8/2015 Machine Intelligence Unit, ISI 1

Soft Computing: Excursion

through Fuzzy Sets, Neural

Networks, Genetic Algorithms,

and Rough Sets

SUSHMITA MITRA

Machine Intelligence Unit

Indian Statistical Institute

Email: [email protected]


Contents

Pattern Recognition

Soft Computing

Fuzzy Sets

Artificial Neural Networks

Genetic Algorithms

Rough Sets

Role in Biomedical Image Analysis

Radiomics & Radiogenomics


Basics of Pattern Recognition

Classification: Supervised learning

Clustering: Unsupervised learning

Feature selection/ Feature extraction


Classification: predicts categorical class labels

classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data

Prediction: models continuous-valued functions, i.e., predicts unknown

or missing values

Typical Applications credit approval

target marketing

medical diagnosis

treatment effectiveness analysis

Classification vs. Prediction


Supervised vs. Unsupervised Learning

Supervised learning (classification)

Supervision: The training data (observations,

measurements, etc.) are accompanied by labels indicating

the class of the observations

New data is classified based on the training set

Unsupervised learning (clustering)

The class labels of training data is unknown

Given a set of measurements, observations, etc. with the

aim of establishing the existence of classes or clusters in

the data


M : Height, Weight, Complexion, Diet….

…..

….……

xxxxxxxxxxx

xxxx B

P

F:

Weight

Height

D : Straight Line

D Classifier Design


Classification methods

Goal: Predict class Ci = f(x1, x2, .. Xn)

Regression: (linear or any other polynomial)

a*x1 + b*x2 + c = Ci.

Nearest neighour

Decision tree classifier: divide decision space

into piecewise constant regions.

Probabilistic/generative models

Neural networks: partition by non-linear

boundaries


Pictorially…

Inter-cluster distances are maximized

Intra-cluster distances are

minimized


What Is Good Clustering?

A good clustering method will produce high quality

clusters with

high intra-class similarity

low inter-class similarity

The quality of a clustering result depends on both

the similarity measure used by the method and its

implementation.

The quality of a clustering method is also measured

by its ability to discover some or all of the hidden

patterns.


Two different K-means Clusterings

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

2.5

3

x

y

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

2.5

3

x

y

Sub-optimal Clustering

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

2.5

3

x

y

Optimal Clustering

Original Points

Soft Computing


Soft Computing

Consortium of methodologies that works

synergistically and provides flexible information

processing

Exploits tolerance for imprecision, uncertainty,

approximate reasoning and partial truth to

achieve tractability, low cost solutions and close

resemblance with human-like decision making

Provides acceptable solution at low cost(car parking example)


Main Components

Fuzzy Sets


Genetic Algorithms

Rough Sets

Fuzzy Sets

Role of Fuzzy Sets (FS)

Modeling of imprecise/qualitative knowledge

Transmission and handling of uncertainties at

various stages

Supporting, to an extent, human type

reasoning in natural form

Understandability of patterns

Providing approximate solution faster

Precision vs. Imprecision

Lines 2-3 m long

Convex areas

People 7 ft. tall

Full membership

Crisp sets: {0,1}

Crisp models

Probability

Fairly short lines

Almost convex areas

People about 7 ft. tall

Partial membership

Fuzzy sets: [0,1]

Fuzzy models

Membership

Probabilities vs. Possibilities


When a tiger is about to pounce, only one thing is certain:

If you take time to compute probabilities, you'll be eaten.

but if you decide in a split second whether to climb

(i) the tree to the right or

(ii) the one ahead,

Then you have used possibilities

(as in measures - one uses the min/max operations).

0.0

0.5

1.0

x

x

0.0

0.5

1.0

x

x

S – membership function

1.0

0.5

0.0

x

1.0

0.5

0.0

x

1.0

0.5

0.0

x

- membership function

SINGULAR VS. GRANULAR VALUES

7.3% high

102.5 very high

160/80 high

Singular Granular

unemployment

temperature

blood pressure

GRANULATION OF A VARIABLE

continuous quantized granulated

Example: Age

quantized Age0

1

µ1

0

young

middle

-aged old

Age

µ

granulated


Background

Analogous to Biological Nervous System


Salient Features

Biological neuron: Impulse triggered by cell, travels along axon – releases chemical transmitter raising/lowering electric potential – if greater than threshold, causes a pulse

Neural network: Not programmed, learn by examples; non-parametric

More efficient than powerful digital computers (number-crunchers) in cognitive tasks


Primary task of all biological neural systems is to control various

functions (mainly behavioral).

Human being can do it almost instantaneously and without much

effort. e.g., recognizing a scene or music immediately.

Artificial Neural Network (ANN) or Neural Network (NN)

models try to simulate the biological neural network with electronic circuitry.

Also known as Connectionists Model/ Parallel Distributed

Processing (PDP).

Purpose : To achieve human-like performance particularly in

cognitive tasks like pattern recognition & image processing.


Definition : Massively parallel interconnected network of

simple (usually adaptive) processing elements that interact with

objects of the real world in a manner similar to biological systems.

NN models are extreme simplifications of human neural

systems.

Computational elements (neurons/nodes/ processors) are

analogous to that of the fundamental constituents (neurons) of

the biological nervous system.

Definition


Comparison

Von Neumann machines: single, fast, powerful CPU; error in 1 bit affects result; memory

Parallel processing architecture: multiple, complex CPUs; shared memory

ANN: very simple, slow processing elements; massively parallel interconnections; graceful degradation (fault tolerance); no memory –information encoded redundantly, distributed among connection weights


Brief History

Rosenblatt (’58-’61): Perceptron --

Linear separability

Minsky & Papert (’68): XOR problem

Rumelhart, Hinton, Williams (’86):

Multilayer Perceptron (MLP) with

backpropagation of error


Processing units

Receives input from connected neurons, compute an output

value and sends it to other connected neurons.

Three types of units - input, output, hidden.

Output value - oi(t)=f(Ii(t))

Total input for ith neuron is Ii.

f is a threshold or squashing function.

Unidirectional connections (wij)

wij < 0 unit uj inhibits unit ui.

wij = 0 unit uj has no direct effect on unit ui.

wij > 0 unit uj excites unit ui.

General framework of neural networks


Learn from example - shown a set of inputs, they self-

adjust to produce consistent response.

Generalize from previous examples to new ones - once

trained, a network's response is mostly insensitive to

variations in input.

Cross validation and Over learning;

Abstract essential characteristics from inputs - find the

ideals (prototype) from imperfect inputs.

Feedforward (no loops) /Feedback (recurrent)

Characteristics of neural networks


Types of Learning

Three learning paradigms :

Supervised (with desired output during training)

Unsupervised (without desired output during training)

Reinforcement ( “reward” -- reinforcement signal for correct output)

Various learning rules :

Error correction

Boltzmann learning

Hebbian learning

Competitive learning


Benefits of Neural Networks

Nonlinearity : To model a physical phenomenon which is in general nonlinear

Adaptivity : To cope with change in environment

Massive parallelism : To enable faster computation

Robustness : Ability to handle missing, confusing and/or noisy data

Fault tolerance : Ability to work, at least to some extent, even if in component failure

Input-output mapping : Ability to model an arbitrary input-output mapping


Some common feature are there; but differ in finer details.

Multi-layer perceptron (hetero associator/supervised classifier)

Hopfield's model of associative memory

Kohonen's model of self-organizing neural network

(regularity detector/ unsupervised classifier)

Radial basis function network (supervised)

Adaptive resonance theory (regularity detector) -- ART

Cellular neural network

Neocognitron

Popularly used NN models


Fields of applications PRclassification, clustering, feature

evaluation,

image preprocessing,

character recognition,

speech recognition,

retrieval by content,

function approximation, optimization, prediction

data mining,

expert system design/rule generation,

financial forecasting,

bioinformatics.


Perceptron

Single layer perceptron: A single layer of neurons

connected by weights to a set of inputs.

THRESHOLD

OUTx2

x1

xn

w2

w1

wn

Let x1,x2,…,xn be the set of inputs

and w1,w2,…,wn be the weights.

If wixi > then the output is 1

else 0, where = threshold

wixi = separating linex1

x2


x2

x1

xn

OUT1

OUT2

OUTn

Multiple decision linesx2

x1


Learning rule

Learning: Present a set of input patterns, adjust the weights

until the desired output occurs for each of them.

wi(t+1) = wi(t) + i;

i = xi;

= T – A (i.e., target – actual).

If the sets of patterns are linearly separable, the single

layer perceptron algorithm is guaranteed to find a separating

hyperplane in a finite number of steps.

Reward if correct, punish if incorrect by updating


Overcoming constraint of linear separability

Cascading layersTwo layers

Generates convex decision regions

S1

S2

X

Y LAYER 2 NEURON

IS 1 ONLY IN

THIS REGION

w11

w12w21

w22

S1

S2

LAYER 1

LAYER 2X

Y


Three layers

Decision regions of any shape

TRIANGLE

ATRIANGLE

B

NON –CONVEX REGION A AND NOT B

LAYER 1 LAYER 2

LAYER 3

Y

X1

X2

X3

Multi-layer networks


Multilayer perceptron

OUTPUT LAYER

HIDDEN LAYER

INPUT LAYER

INPUT PATTERN

OUTPUT PATTERN

Wkj

Wji

k

j

i


Nodes of two different consecutive layers are

connected by links or weights.

There is no connection among the elements of the same

layer.

The total input (Ii) to the ith unit

Ii =

oj is the output of the jth neuron.

j

jijow


The output of a node i is obtained as

oi = f(Ii), f is the activation function.

Mostly the activation function is sigmoidal/squashing,

with the form,

f(x) = 1/(1+e-(x-)/0).

f(x)

0 2-2-4 4

0.5

1.0

x

Initially very small random values assigned to weights.


Parameter updating

For learning (training) we present the input pattern X={xi}, and ask

the net to adjust its set of weights/biases in the connecting links such

that the desired output T={ti} is obtained at the output layer.

Then another pair of X and T is presented for learning.

Learning tries to find a simple set of weights and biases that will be

able to discriminate among all the input/output pairs presented to it.

The output {oi} will not be the same as the target {ti}.

Error is, E = 2

For learning the correct set of weights error is E is reduced as

rapidly as possible.

Use gradient descent technique.

i

ii ot )(2

1


Neuro-Fuzzy Hybridization


Neuro-Fuzzy Hybridization

ANN emulates

architecture and

information

representation of

human brain

FS mimics human

reasoning and

uncertainty handling

FNN: ANN equipped

with capability of

handling fuzzy

information

NFS: Fuzzy system

augmemented by

ANN to enhance its

flexibility, speed and

adaptibility


Generic & Application-specific merits

ANN: massive

parallelism, robustness,

learning

FL: modeling imprecise

qualitative knowledge,

and transmission of

uncertainty

ANN: generating

highly nonlinear

decision regions

FL: handling

uncertainty in input

description and

output decision


(for More Intelligent System)

Neuro-Fuzzy Computing


Integration Schemes

Fuzziness at input and/or output levels

ANN implements fuzzy decision system

Fuzziness at neuronal level

Measures of fuzziness used as error or

energy function

Genetic Algorithms

Role of GAs

Adaptive, robust, efficient,

global search methods

suitable in large search

space.

Select a model based on

optimizing some preference

criterion/objective function.

Useful in regression.

GAs are adaptive and robust computational procedures modeled on the

mechanics of natural genetic systems. They act as biological metaphor

and try to emulate some of the processes observed in natural evolution.

Natural evolution operates on encoding of biological entities in the

form of a collection of genes called a chromosome. Similarly, GAs

operate on string representation of possible solutions (individuals/

chromosomes) containing the features.

Selection : obeys Darwinian survival of the fittest strategy

Nature acts as environment. Objective function plays the role of

environment.

Variation is introduced mainly through genetic operations like

recombination (crossover) and mutation.

What are genetic algorithms?

Population of individuals

Termination conditions

Replacement technique

Probabilities to perform genetic operations

Genetic operators (recombination/crossover, mutation)

Selection procedure

Objective function & associated fitness evaluation criterion

Encoding/decoding (of individuals) mechanism

Components of a GA

Coding

Converts parameter values into chromosomal representation

For the continuous valued parameters decimal to the binary conversion

used.

For example 13 == 01101 (for 5 bit representation )

For a parameter having categorical values a particular bit position in the

chromosomal representation is set to 1 if it comes from that category.

For example the parameter marital status can have values from {married,

unmarried, divorced, widow}. So, unmarried == 0100 widow==0001

These strings (representing the parameters of a problem) are concatenated

to form a chromosome.

Encoding/decoding mechanism

It is the reverse of encoding.

For continuous valued parameter the binary representation is converted to

continuous value by the following formula

01101==40+(13/31)*(60-40)=48.387

For categorical valued parameters the value is found by consulting the

range of the parameter.

0001== widow

0100== unmarried

i=0

bits used-1

biti*2i

2(bits used)-1Lower bound + *(Upper bound - Lower

bound)

Decoding

A measure of chromosome's performance. More suitable

strings should get high fitness values.

Evaluation and selection

Selection gives more chance to better fitted individuals

(Mimics natural selection procedure)

Popular selection techniques

Roulette wheel selection

Stochastic universal sampling

Linear normalization selection

Tournament selection

Roulette wheel selection

Sum the fitness of all the chromosomes of the population.

Call it total-fitness.

Generate a random number n in [0, total-fitness]

Return the first chromosome whose fitness when added to

the fitness of the preceding population members is

greater than equal to n.

Example:

Let there be five chromosomes with fitness 20, 10, 40, 3, 18

Then total-fitness=91.

Say, the random number drawn (n) is 45.

Select the 3rd chromosome (since 20+10+40 > 45).

Choose mating pairs (from the selected chromosomes).

Check (using pc) whether this pair should go for

recombination or not. If yes, interchange chromosome

segments. one point, two point, multi point, uniform,...

Recombination/crossover

One point crossover

parent1: xy xy x y xy child1: xy xy x b ab

parent2: ab ab a b ab child2: ab ab a y xy

before

crossoverafter crossover

Recombination (contd.)

Two point crossover

parent1: xy xy x y xy child1: xy ab a y xy

parent2: ab ab a b ab child2: ab xy x b ab

before

crossoverafter

crossover

Introduces diversity.

Bit mutation.

Check (using pm) whether this bit should be

mutated or not. If yes, flip the bit.

001000 000000

Probabilities to perform genetic operations

May be fixed or made variable.

pc : 0.6 to 0.9 pm : 0.001 to 0.01

Mutation

Execute for a fixed number of generations/iterations.

Until a string with a certain fitness value is located.

Until the population attains a certain degree of

homogeneity (most of the individuals become similar).

Elitism (optional)

Keeps track of /store the best solution obtained so far.

Terminating criterion

Applications

Numerical, combinatorial and constrained optimization

Scheduling, TSP, Graph Coloring

Automatic programming - evolves computer programs for

specific tasks (Genetic Programming)

Pattern recognition - classification, clustering, prediction

Image processing --- segmentation, enhancement

Data mining --- rule mining, clustering

Immune systems - discovery of multigene, multiagent

systems

Economics - financial prediction, bidding strategies

Rough Sets

Rough Sets (RS)

Major mathematical tool for managing uncertainty

that arises from granularity in the domain of

discourse, i.e., from the indiscernibility between

objects in a set;

proved to be useful in a variety of KDD

processes;

offers mathematical tools to discover hidden

patterns in data (relevant to DM).

Used for knowledge extraction and encoding of

knowledge-based networks.

Principles of RS

Discovers redundancies and dependenciesbetween the features of a problem to be classified.

Approximates a given concept from below and from above, using lower and upper approximations.

These approximations are defined by the angle from which the set is viewed, i.e. through the information provided by the selected attributes (features).

Rough sets can be used to extract a set of rules in IF-THEN form, from crude domain knowledge, using decision table.

Rough Sets

. x

Upper

Approximation BX

Set X

Lower

Approximation BX

[x]B (Granules)

[x]B = set of all points belonging to the same granule as of the point xin feature space WB.

[x]B is the set of all points which are indiscernible with point x

in terms of feature subset B.

UB W

Knowledge extraction using RS

Create a knowledge base, classifying

objects and attributes within decision

tables.

Remove some undesirable attributes

(knowledge discovery).

Analyze data dependency in the reduced

database to find the minimal subset of

attributes called reduct.

Rough Clusters

A pattern can belong to at most one lower

approximation

A member in a lower approximation is also a member

in the corresponding upper approximation

If a pattern does not belong to any lower

approximation, then it must belong to two or more

upper approximations

Indian Statistical Institute, Kolkata 69

Basic Idea


dmin

d1

IF d1-dmin < threshold

THEN upper approximation of U2 and U1

ELSE lower and upper approximation of U2

v2

v1 Cluster Center

Pattern

The objects (or patterns) are assigned to a lower or an upper

approximation of a cluster as follows.

Fuzzy vs Rough

Fuzzy sets: Uncertainty in overlapping

regions modeled by membership values,

based on similarity relations.

Rough sets: Uncertainty due to granularity

from indiscernibility modeled by lower and

upper approximations. Ambiguity due to

absence of complete information.


Role in Biomedical Image

Analysis


Natural computing, through nature-inspired

strategies, plays a major role in intelligent

decision-making systems – with reference to

segmentation, classification, feature

extraction & selection.

Radiographic imaging modalities – like CT,

MRI, PET – help in providing improved

diagnosis, prognosis and treatment planning

for improved tumor management.


Imperfect knowledge in medicine

Incomplete understanding of biological

mechanisms

Imprecise test measurements

Uncertainty of normal ranges for test results

Simultaneous presence of more than one

disease condition

Missing information


Quantitative imaging

Computed tomography (CT) scans produce

tomographic (cross-sectional) slices, horizontally

& vertically, and are more detailed than X-rays.

Positron emission tomography (PET) detects

functional changes in a tissue (by tracing

positron-emitting radioactive tracer FDG – with

cancer cells using glucose faster) before related

anatomical changes become visible.

Magnetic resonance imaging (MRI) uses strong

magnetic field to make body tissues emit their

own radio waves of differing intensities.


CT scan image for non small cell lung cancer


Rough c-Means: Basic Idea


dmin

d1

IF d1-dmin < threshold

THEN upper approximation of U2 and U1

ELSE lower and upper approximation of U2

v2

v1 Cluster Center

Pattern

The objects (or patterns) are assigned to a lower or an upper

approximation of a cluster as follows.

Rough-Fuzzy:

incorporate

membership µ

(maximum)

Rough-Fuzzy c-MeansMitra S, Banka H, Pedrycz W, IEEE Trans. SMC, Part B, vol. 36, pp. 795-805, 2006

Assign each pattern to the lower approximation or upper approximations of cluster pairs by computing fuzzy membership difference from cluster centroid pairs. Sort in descending form

If the difference is less than threshold

then assign to both upper approximation pairs

else assign it to the ith lower approximation such thatis maximum over c clusters

Compute new mean

for each cluster and

repeat until

convergence


kX iUB

ji UBUB , ji UU ,

kX

iku

otherwiseu

Xu

UBUBUBifu

Xu

UBUBUBifu

Xu

wu

Xu

w

ik

ik

iik

iik

iik

iik

ik

ik

UBX

m

ik

UBX

k

m

ik

iii

UBUBX

m

ik

UBUBX

k

m

ik

iii

UBUBX

m

ik

UBUBX

k

m

ik

up

UBX

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ApplicationS. Mitra and B. Barman, LNAI Vol. 5009, pp. 300-307, 2008.

Novel Application of Rough-Fuzzy (RF)Clustering (For Synthetic as well as CT scanimages of the brain)

RF Clustering simultaneously handles overlapof clusters (Fuzzy) and uncertainty involved inclass boundary (Rough)

Number of clusters was optimized via clustervalidity indices

Main objective was the diagnosis of the extentof brain infarction in CT scan images


CT Scan Image Segmentation

Segmentation – Process of partitioning animage into some non-overlappingmeaningful regions

Segmentation here via Pixel Clustering

Study consists of cases of VascularInfarction of the Human Brain

Partitioning into five regions – Gray matter(GM), White matter (WM), Infarctedregion, Skull and the backround


Fresh case of Vascular Insult (Original Image)


1. Infarction is on the

left side.

2. The left side is

compressing the right

side

3. Dilation of the blood

ventricles

4. Severe edema

5. Division of brain into

gray matter, white

matter and the

cerebrospinal fluid

(CSF)

6. The third ventricle is

not visible here due

to severe edema

from the right

ventricle side

7. Cause: Cholesterol

Deposit, Blockage

Segmentation Result (RFCM)

White Matter

Gray Matter

Cerebrospinal Fluid

Infarcted Region

Comparative Analysis

(HCM, FCM, RCM & RFCM)


RFCM: Better segmentation

of White matter, Gray matter

and CSF.

HCM (Noisy)FCM (Noisy)

RCM

(Noisy)

Skull

Gray Matter

White Matter

CerebroSpinal Fluid

Chronic Infarction (Original Image)


1. Patient suffering

from vascular insult

2. Right and left

should have been

symmetric (the most

definite metric for

comparison)

3. Right side is dark

because it has not

received blood

supply for a very

long time

4. Due to this the

blood ventricles

have dilated and

have undergone

liquefaction (water)

5. Parenchyma is

infarcted

6. Arteries were

blocked due to high

cholesterol levels

7. Happens due to

normal old age

Chronic Infarction (RFCM Segmentation

result)


White Matter

Gray Matter

Cerebrospinal Fluid

Infarcted Region

Skull

Subtle Case of Infarction (Original Image)


1. The third ventricle

has dilated

2. Edema from below

3. Blockage of arteries,

no blood supply from

a long time

4. Dilation of left and

right ventricles due to

this as passage from

below is blocked

5. Problem modeling

same. although the

infarction here is

petty difficult to

locate

6. Tough problem of

segmentation for

infarction

7. Cause: Cholesterol

deposit, Blockage

Subtle Case of Infarction (RFCM

Segmentation result)


Cerebrospinal Fluid

White Matter Uniform

merging of Gray Matter and the Infarcted region

Remarks

In the absence of an accurate index to test theaccuracy of segmentation results in CT scanimagery, we resorted to expert domain knowledge

36 frames of each case of infarction was studiedand results verified by an experienced radiologist

RFCM produced the best result as verified by expertradiologist

Results promise to provide a helpful second opinionto radiologists in case of Computer-AidedDiagnostic (CAD)


Radiomics

High throughput extraction and analysis of advanced quantitative imaging features from medical images.

In cancer, several studies have reported the statistical association between the advanced imaging features extracted from the tumor regions of the routine medical images and the tumor stage, metabolism, patient survival and underlying gene expression patterns.

To ensure the reliability of quantitative imaging features, accurate and robust tumor delineation is essential. Tumor segmentation is one of the main challenges of Radiomics, as manual delineation is prone to high inter-observer variability.

It has been shown that semiautomatic segmentation approaches efficiently reduce inter-observer variability as compared to the time consuming manual delineations.

Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho, S., Mak, R. H., Mitra, S.,Uma Shankar, B., Kikinis, R., Haibe-Kains, B., Lambin, P., Aerts, H. J. W. L. (2014) Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE 9(7): e102107. doi:10.1371/journal.pone.0102107

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102107

Objective Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho, S., Mak, R. H., Mitra, S., Uma

Shankar, B., Kikinis, R., Haibe-Kains, B., Lambin, P., Aerts, H. J. W. L. (2014) Robust Radiomics

feature quantification using semiautomatic volumetric segmentation. PLoS ONE 9(7): e102107.

doi:10.1371/journal.pone.0102107


In this study, a semiautomatic volumetric segmentation algorithm, available in the

free and publicly available 3D-Slicer platform, was investigated in terms of its

robustness for Radiomics feature quantification.

We extracted fifty-six CT 3D-Radiomic features from 3D-Slicer segmentations made

by three independent observers, twice, and compared them to the features extracted

from manual delineations provided by five independent physicians.

The assessed fifty-six 3D-radiomic features quantified I) tumor intensity, II) tumor

shape, and III) tumor texture. Intra-class correlation coefficient was used to quantify

the reproducibility of imaging features across observers.

It was observed that quantitative imaging features extracted from semi-automatically

segmented tumors have lower variability and are more robust as compared to

features extracted from manual tumor delineations.

Figure 1. Schematic diagram depicting the overview of the analysis.

Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, et al. (2014) Robust Radiomics Feature Quantification Using Semiautomatic

Volumetric Segmentation. PLoS ONE 9(7): e102107. doi:10.1371/journal.pone.0102107



Semiautomatic segmentation in 3D slicer

GrowCut algorithm implemented in 3D-Slicer was used (www.slicer.org). GrowCut is

an interactive region growing segmentation strategy. Given an initial set of label

points the algorithm automatically segments the remaining image.

For N-class segmentation, the algorithm needs N initial sets of labeled pixels (one set

corresponding to each class) from the user. Based on these, it automatically

generates the region of interest (ROI), which is the convex hull of the user-labeled

pixels with an additional margin.

Next, it iteratively labels all the remaining pixels in the ROI utilizing user-supplied

pixel labels. Individual pixels are labeled by computing a weighted similarity metric of

a pixel with all its neighbors, where the weights correspond to the neighboring pixel's

strength.

The algorithm converges when all the pixels in the ROI have unchanged labels

across several iterations.

http://www.slicer.org/

3D-Slicer provides a graphical user interface (GUI) as the front end and an efficient

algorithm as the back end for the GrowCut segmentation. After loading the patient

data, the process begins with the user initialization of the foreground and background

by manually marking the area inside and outside the tumor region. Next, the Growcut

automatic competing region-growing algorithm gets activated, and segments the ROI

into foreground and background regions. Thereafter, background and the surrounding

isolated foreground pixels are removed following visual inspection.

Intra-class correlation coefficient (ICC) was calculated in order to quantify the feature

reproducibility. The ICC is a statistical measure, ranging between 0 and 1, indicating

null and perfect reproducibility, respectively.

Since two 3D-Slicer segmentations from each of the three observers were considered

for the analysis, the six 3D-Slicer segmentations were divided in to two sets, each

having three segmentations (one from each observer).

Box-plot comparing intra- and inter-observer reproducibility (ICC) of radiomics features.

Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, et al. (2014) Robust Radiomics Feature Quantification Using Semiautomatic

Volumetric Segmentation. PLoS ONE 9(7): e102107. doi:10.1371/journal.pone.0102107



Natural Computing in Medical

Image Analysis

S. Mitra and B. Uma Shankar,

``Medical image analysis for cancer

management in natural computing

framework", Information Sciences, Vol.

306, pp. 111-131, 2015.

• Segmentation: Fuzzy sets,

neural nets, rough sets, GA

• Feature extraction: Texture, co-

occurrence matrix, shape,

intensity gradient, statistical,

wavelets, color

• Classification: Neural nets,

fuzzy sets, GA, swarm

intelligence, support vector

machine


Personalized Medicine

Offers RIGHT drug

To the RIGHT disease

At the RIGHT time

With the RIGHT dosage


Radiogenomics Radiographic imaging features can serve as molecular

surrogates (substitutes) of gene expression patterns, thereby

contributing towards diagnosis, prognosis, and gene--

associated treatment response of various forms of cancer.

It is observed that tumors having greater genomic

heterogeneity are more likely to develop treatment resistance

and are prone to faster metastasis, thereby resulting in poorer

prognosis.

The genomic heterogeneity, translated into intra-tumoral

heterogeneity, can be potentially captured through medical

imaging in a non-invasive manner.

Such association has the potential for a patient-specific

personalized management of tumors


Radiogenomics and Personalized MedicineS. Mitra and B. Uma Shankar, ''Integrating radio imaging with gene expressions towards a

personalized management of cancer", IEEE Transactions on Human-Machine Systems, 44, 664-677, 2014.


Summary

Soft Computing: Fuzzy sets, Artificial neural

networks, genetic algorithms, rough sets

Role in Biomedical Image Analysis

Radiomics

Radiogenomics

Thank You
