Lecture 2 Flashcards

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1
Q

Labelling

A
  • After detecting foreground regions, label them
  • Find group of pixels that connect to each other -> connected component analysis
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2
Q

Connectivity

A
  • 2D
    o 4-connectivity: any two pixels that have an Euclidean distance D = 1
    o 8-connectivity: any two pixels that have an Euclidean distance D < 2
  • 3D
    o 6-connectivity: any two pixels that have an Euclidean distance D = 1
    o 26-connectivity: any two pixels that have an Euclidean
    distance D < 2
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3
Q

Connected Component Analysis

A
  • Two pass blob colour algorithm
  • Forward pass: labelling takes into account connections to already
    labelled pixels and keeps track of connections
  • Keep track of connected region using connectivity table
  • Backward pass: replace labels with lowest connected label
    o In the example on the right we thus get labels 1 and 3
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4
Q

Region Growing Algorithm

A
  • Start from seed point (coordinates in an image)
    o Has some intensity value
  • Check neighbours of the seed point, given a connectivity measure
  • If neighbour fulfils some criteria it is included in the segmentation
    o For example similar texture or intensity
    o Usually requires use of a (double) threshold
  • Repeat previous steps for all neighbours included in the segmentation
    o Segmented region grows this way
  • Iterate until no improvements are made
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5
Q

Segmentation Performance

A
  • Class detection can have 4 different results, assuming the foreground is the positive class and
    the background is the negative class
    o True positive (TP): foreground pixel labelled as foreground
    o True negative (TN): background pixel labelled as background
    o False positive (FP): background pixel labelled as foreground
    o False negative (FN): foreground pixel labelled as background
  • Accuracy: (TP + TN) / (TP + TN + FP + FN)
    o Problematic because we often have extremely imbalanced classes
  • Specificity: TN / (TN + FP)
  • Sensitivity/recall: TP / (TP + FN)
  • Dice coefficient: 2TP / (2TP + FP + FN)
  • Jaccard measure: TP / (TP + FP + FN) = Intersection over Union (IoU)
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6
Q

Classification

A
  • First-order statistical texture analysis -> histogram,
    typically normalised
  • Can extract six features, shown on the right
  • Does not consider spatial relationship and
    correlation between pixels
    o Identical histograms can belong to different textures
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7
Q

Local Binary Pattern

A
  • Takes a centre pixel gc and P neighbours gp at a distance R
    o Now takes spatial relations into account for classification
  • Can also use K-nearest neighbours
    o But need to normalise features
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8
Q

Gabor filters

A
  • Multiply Gaussian kernel with sinusoidal function
    o Can change frequency of sinusoidal function
    o Can change orientation of sine wave
    o Can change scale (sigma) of the Gaussian function
  • Real and imaginary component representing orthogonal directions
  • Can create a bank of filters by varying parameters of filters
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9
Q

Classification Performance Metrics

A
  • Each classified case gets a likelihood score
  • Often use ROC curves for analysis
  • Check all thresholds for classification
    o For each, calculate sensitivity and 1-sensitivity
    o Gives a point in a 2D space, make curve through all points
  • Calculate Area Under the Curve (AUC) for ROC curves
    o Range [0,1]
    o Identity line (AUC = 0.5) indicates random chance
    o Also seen as the probability that the model ranks a random positive example more
    highly than a random negative sample
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10
Q

Detection

A
  • Answers question such as: are there any abnormal structures in this image?
  • Feature based
    o Extract relevant features for every location
    o Combine them (e.g. machine learning)
    o Threshold result (post-processing)
  • Classifier: trained by combining several features
    o After classification, each detection gets a likelihood value
    o Then perform threshold and post-processing
  • Post-processing
    o Morphology to remove small areas
    o Discard detection that are too small/large
    ▪ Can be learned from training data
    o Use prior knowledge to discard highly unlikely detected features
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11
Q

Template Matching

A
  • When you need to find a known object in an image
    o Can build a matched filter for appearance of the object
    ▪ High responses when doing convolution with matched filter and image
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12
Q

Detection Performance Metrics

A
  • Need to define a hit criterion
    o If d < 2r -> hit the object
    o Ensures that we do not need an exact match
  • Sensitivity/recall (R) = TP / (TP+FN)
  • Precision (P) = TP / (FP+TP)
  • F1-score = 2PR (P+R) -> Balance between precision and recall
  • Since each detection has a likelihood value, we can threshold to select
    detections
    o Influences TP, FP,FN
  • Free-response receiver operating characteristic (FROC)
    o Like ROC curves, but now sensitivity vs average
    number of false positives
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