Method of directed feature development for image pattern recognition

Abstract

A computerized directed feature development method receives an initial feature list, a learning image and object masks. Interactive feature enhancement is performed by human to generate feature recipe. The Interactive feature enhancement includes a visual profiling selection method and a contrast boosting method.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiment and other aspects of the invention will become apparent from the following detailed description of the invention when read in conjunction with the accompanying drawings, which are provided for the purpose of describing embodiments of the invention and not for limiting same, in which:

FIG. 1 shows the processing flow for the application scenario of the interactive feature enhancement method;

FIG. 2 shows the sequential processing flow for the interactive feature enhancement method;

FIG. 3 shows the processing flow for the visual profiling selection method;

FIG. 4 shows the processing flow for the object montage creation method;

FIG. 5A shows an example image of cell nuclei;

FIG. 5B shows the object masks for the image in FIG. 5A;

FIG. 5C shows the object montage of a subset of the objects shown in FIG. 5B;

FIG. 6 shows the processing flow chart for the histogram creation method;

FIG. 7A shows the histogram plot of a feature for the objects shown in FIG. 5B;

FIG. 7B shows a bin of the histogram plot of FIG. 7A is selected and highlighted;

FIG. 8 shows the processing flow for the user interface method;

FIG. 9 shows the processing flow for the contrast boosting feature optimization method;

FIG. 10A shows an example object montage display;

FIG. 10B shows an updated montage of FIG. 10A where the extreme objects are highlighted by framing;

FIG. 11 shows the processing flow for the contrast boosting feature generation method.

DETAILED DESCRIPTION OF THE INVENTION

I. Application Scenario

The application scenario of the directed feature development method is shown in FIG. 1. As shown in the figure, learning image 100, object masks 104, and initial feature list 102 are processed by a feature measurement step 112 implemented in a computer. The feature measurement step 112 generates initial features from the input feature list 102 using the learning image 100 and the object masks 104. The object masks are results from image segmentation such as image thresholding or other methods.

In one embodiment of the invention, the initial features 106 include

• • Morphology features such as area, perimeter, major and minor axis lengths, compactness, shape score, etc.
  • Intensity features such as mean, standard deviation, intensity percentile values, etc.
  • Texture features such as co-occurrence matrix derived features, edge density, run-length derived features, etc.
  • Contrast features such as object and background intensity ratio, object and background texture ratio, etc.

The initial features 106 along with the initial feature list 102, the learning image 100 and the object masks 104 are processed by the interactive feature enhancement step 114 of the invention to generate feature recipe 108. In one embodiment of the invention, the feature recipe contains a subset of the salient features that are selected as most relevant and useful for the applications. In another embodiment of the invention, the feature recipe includes the rules for new feature generation.

The interactive feature enhancement method further consists of a visual profiling selection step for interactive salient feature selection and a contrast boosting step for new feature generation. The two steps could be performed independently or sequentially. The sequential processing flow is shown in FIG. 2.

As shown in FIG. 2, the visual profiling selection step 206 processes the learning image 100, initial features 106, initial feature list 102 and object masks 104 and selects subset of initial features as subset features 200 by human 110. The subset features 200 along with the learning image 100 and object masks 104 are processed by the contrast boosting step 208 to generate optimized features 202. The optimized features 202 contain further selection of subset features and newly generated features. New feature generation rules 204 are also outputted from this step.

II. Visual Profiling Selection

The visual profiling selection method allows the input from human application knowledge through visual examination without the need for human's understanding of the mathematical formula underlying the feature calculation. The processing flow for the visual profiling selection method is shown in FIG. 3. The initial features 106 are processed by a information measurement step 320 to generate information scores 300, at least one for each feature. The information scores 300 measure the information content for the initial features 106 on the initial feature list 102. The initial feature list 102 and the corresponding information scores 300 are processed by a ranking step 322 to generate a ranked feature list 304. The ranked feature list 304 is presented to human 110 through the user interface 324. The human 110 provides profiling feature 306 selection. The selected profiling feature 306 is processed by an object sorting step 326 that sorts the initial features 106 associated with the profiling feature 306. The object sorting step 326 sorts the initial profiling feature values and generate an object sequence 308 and their associated object feature values 310. The object sequence 308 and its associated object feature values 310, the learning image 100 and the object masks 104 are processed by the object montage creation step 330 to generate object montage display 316 according to the object sequence 308. The object montage display 316 is presented to the user interface 324 for human 110 visual examination and the selection of subset features 200. An optional histogram creation step 328 is also provided. The histogram creation step 328 inputs the object feature values 310 and generates a histogram plot 312 for displaying to human 110 through the user interface 200. The human 110 could select bin 314 from the user interface 324 that will be highlighted on the histogram plot 312 by the histogram creation step 328. Also, objects can be selected either from the histogram plot 312 or from the object montage display 316. The selected objects 318 are highlighted in the object montage display 316 by the object montage creation step 330.

II.1 Information Measurement

The initial features contain the feature distributions for the learning objects. The information measurement method of this invention measures the information content of the feature distribution to generate at least one information score. In one embodiment of the invention, the information content such as coefficient of variation (standard deviation divided by mean) is used for the information score. In another embodiment of the invention, signal percentage is used as the information score measurement. The signal objects are objects whose feature values are greater than mean * (1+α) or are leas than mean * (1−α). Where α is a pre-defined factor such as 0.2.

When the objects are labeled as two classes, the one-dimensional class separation measures can be used for the information score. We can define between-class variance σ²_b, within-class variance σ²_w, and mixture class variance σ²_m. Common class separation measures include S₁/S₂, ln|S₁|−ln|S₂|, sqrt(S₁)/ Sqrt(S₂), etc. Where S₁ and S₁ are one of between-class variance σ²_b, within-class variance σ²_w, and mixture variance σ²_m (Keinosuke Fukunaga “Statistical Pattern Recognition”, 2^nd Edition, Morgan Kaufmann, 1990 P. 446-447).

In another embodiment of the invention, the unlabeled data can be divided into two classes by a threshold. The threshold could be determined by maximizing the value:

(N_L×m_L²)+(N_H×m_H²)

where N_L and N_H are the object counts of the low and high sides of the threshold, and m_L², m_H² are the second order moments on the left and right sides of the threshold. After the two classes are created by thresholding, the above class separation measures could be applied for information scores.

Those ordinary skilled in the art should recognize that other information measurement such as entropy and discriminate analysis measurements could be used as information scores and they are all within the scope of the current invention.

II.2 Ranking

The ranking method 322 inputs the information scores 300 of the features from the initial feature list 102 and ranks them in ascending or descending orders. This results in the ranked feature list 304 output.

II.3 Object Sorting

The object sorting method 326 inputs the profiling feature 306 index and its associated initial features 106 for all learning objects deriving from the learning image 100 and the object masks 104. It sorts the objects according to their profiling feature values in ascending or descending order. This results in the sorted object sequence as well as their object feature values.

II.4 Object Montage Creation

The processing flow for the object montage creation method is shown in FIG. 4. As shown in FIG. 4, an object zone creation step 404 inputs the leaning image 100 and the object masks 104 to generate an object zone 400 for each of the objects in the object masks 104. In one embodiment of the invention, the object zone 400 is a rectangular region of the learning image covering the mask of the object, object Region of Interest (ROI). In another embodiment of the invention, an expanded region of the object ROI is used as the object zone. The object masks 104 could be associated with the object zone so object mask overlay can be provided.

The object zone 400 for each of the objects are processed by an object montage synthesis step 406 that inputs the object sequence 308 to synthesize the object montage containing a plurality of object zones ordered by the object sequence 308 to form an object montage frame 402. An object montage frame 402 is a one-dimensional or two-dimensional frame of object zones where the zones are ordered according to the object sequence 308.

The object mintage frame 402 is processed by an object montage display creation step 408 that associates the object feature values 310 to the object montage frame 402. The object feature values 310 can be hidden or shown by user control through the user interface 324. Also, object zone(s) 400 are highlighted for the selected object(s) 318. The highlight includes either a special indication such as frame drawing or object mask overlay. The object montage frame 402 containing feature value association and selected object highlighting forms the object montage display 316 output.

FIG. 5A shows an example image of cell nuclei. Its object masks are shown in FIG. 5B. An object montage of a subset of the objects in FIG. 5B is shown in FIG. 5C.

II.5 Histogram Creation

The processing flow for the histogram method is shown in FIG. 6. As shown in FIG. 6, a binning step 606 inputs the object feature values 310 to generate the bin ranges 604 and bin counts 600. To determine the bin ranges 604, the number of bins is determined first. The number of bins could be from a pre-set value, from user input, or derived automatically from the object feature value distribution and the object counts. After the number of bins is determined the bin ranges 604 can be defined by either equal quantization or normalized quantization methods that are common in the art. The bin count 600 for a bin can be determined by simply counting the number of objects having feature values fall within the bin range of the corresponding bin. The bin counts 600 are processed by a bar synthesis step 608 to generate bar charts 602 where the number of bars are the same as the number of bins and the heights of the bar charts 602 are scaled according to the maximum bin count 600s. The bar charts 602 and the bin ranges 604 are processed by the histogram plot creation step 610 to generate histogram plot 312 that associates the values in bin ranges and the counts in the histogram plot 312. When the selected bin 314 is inputted, the selected bin(s) 314 in the histogram plot 312 is highlighted.

FIG. 7A shows the histogram plot of a feature for the objects in FIG. 5B. FIG. 7B shows a bin 700 is selected and highlighted with a different pattern.

II.6 User Interface

The user interface step 324 of the invention displays the ranked feature list 304 and their information scores 300 and allows human 110 to select profiling feature 306 for object montage creation 330. The processing flow for the user interface is shown in FIG. 8. As shown in FIG. 8, the ranked feature list 304 and their information scores 300 are processed by an information score ranking display and profiling feature selection step 800. The step shown the information scores of the ranked features to the human 110 for the selection of profiling feature 306 output. The human selected profiling feature 306 is processed by a feature profiling step 802 that shows the object montage display 316 and optionally shows the histogram plot 312 for the feature via a Graphical user interface. The human 110 could select histogram bins and/or select object for highlighting having selected bin 314 and selected object 318 outputs to the object montage creation 330 and the histogram creation 328 steps. The showing of object montage display 316 along with the histogram plot 312 allow human 110 to perform feature selection 804 yielding a subset of salient features after reviewing and visual evaluation from the profiling display. Those ordinary skilled in the art should recognize that the graphical user interface could include standard graphical tools such as zoom, overlay, window resizing, pseudo coloring, etc. The user interface allows visual evaluation and selection of for salient measurements. Human 110 do not have to know the mathematics behind measurement calculation.

III. Contrast Boosting Feature Optimization

The contrast boosting method 208 of the invention allows user re-arrange objects on montage to specify extreme examples. This enables the utilization of application knowledge to guide feature selection. Initial features ranked by contrast between the user specified extreme examples are used for application specific feature selection. New features are generated automatically to boost the contrast between the user specified extreme examples for application specific feature optimization. The processing flow for the contrast boosting feature optimization method is shown in FIG. 9. As shown in FIG. 9, the human 110 performs extreme example specification 906 by re-arranging the object montage display 316. This results in the updated montage 904 output. The updated montage 904 including the extreme examples are used for contrast boosting feature generation 908 using the initial features 106. This outputs new features 900 and new feature generation rules 204. The new features 900 and the initial features 106 are processed by the extreme directed feature ranking step 910 based on the extreme example specified in the undated montage 904. This results in extreme ranked features 902 output. The extreme ranked features 902 are processed by the feature display and selection step 912 to generate optimized features 202 output.

III.1 Extreme Example Specification

This invention allows human 110 to specify extreme examples by visual examination of montage object zones and utilizing application knowledge to guide the re-arrangement of object zones. The extreme example specification 906 is performed by re-arranging the objects in an object montage display 316. In this way, human 110 can guide the new feature generation and selection but do not have to know the mathematics behind computer feature calculation. Human 110 is good at identifying extreme examples of distinctive characteristics yet human 110 is not good at discriminating between borderline cases. Therefore, the extreme example specification 906 requires only human to move obvious extreme objects to the top and bottom of the object montage display 316. Other objects do not have to be moved. In the extreme examples that are moved by human 110, human could sort them according to the human perceived strength of the extreme feature characteristics. The updated object montage display 316 after extreme example specification forms the updated montage 904 output. The updated montage output specifies three populations: extreme 1 objects, extreme 2 objects, and other unspecified objects. FIG. 10A shows an example object montage display. FIG. 10B shows its updated montage where the extreme objects are highlighted by framing. The extreme 1 objects 1000 are located on the top and the extreme 2 objects 1002 are located at the top of the display.

II.2 Contrast Boosting Feature Generation

The contrast boosting feature generation method automatically generates new features by combining a plurality of initial features to boost the contrast between the extreme examples.

In a particularly preferred, yet not limiting embodiment, the present invention uses two initial feature combination for new feature generation, three types of new features are generated:

• • Weighting: Feature_1+boosting_factor*Feature_2
  • Normalization: Feature_1/Feature_2
  • Correlation: Feature_1*Feature_2

The ordinary skilled in the art should recognize that the combination could be performed iteratively using already combined features as the source for new combination. This will generate new features involving more than two initial features without changing the method. To assure that there is no division by zero problem, in one embodiment of the invention, the normalization combination is implemented in the following form:

Feature_1/(Feature_2+α)

Where α is a small non-zero value.

The processing flow for the contrast boosting feature generation is shown in FIG. 11. As shown in FIG. 11, the updated montage 904 and the initial features 106 are processed by a population class construction step 1102 to generate population classes 1100. The population classes 1100 are used for new feature generations 1104 to generate new features 900 and output new feature generation rules 204.

A. Population Class Construction

The updated montage 904 specifies three populations: extreme 1 objects, extreme 2 objects, and other unspecified objects. The population class construction 1102 generates three classes and associate them with the initial features. In the following, we call extreme 1 objects as class 0, extreme 2 objects as class 1, and the other objects as class 2.

B. New Feature Generation

For the new features with fixed combination rules such as:

• • Normalization: Feature_1/Feature_2
  • Correlation: Feature_1*Feature_2the new feature generation is a straightforward combination of initial features. However, some combination rules require the determination of parameter values. For example, the weighting combination method:
  • Weighting: Feature_1+boosting_factor*Feature_2

Requires the determination of the boosting_factor. To determine the parameters, goodness metrics are defined.

Goodness Metric

The goodness metric for contrast boosting consists of two different metrics. The first metric (D) measures the discrimination between class 0 and class 1. The second metric (V) measures the distribution of the class 2 with respect to the distribution of the class 0 and class 1. The metric V estimates the difference between distribution of the class 2 and the distribution of the weighted mean of the class 0 objects and class 1 objects. In one embodiment of the invention, the two metrics include discrimination between class 0 and class 1 (D) and class 2 (V) difference as follows:

$D=\frac{{\left(m_0-m_1\right)}^2}{\sigma _0^2+\sigma _1^2}$ $V=\frac{{\left[\left(m_2-{\mathrm{vm}}_0-\left(1-v\right)m_1\right)\right]}^2}{{\sigma }_2^2+v^2{\sigma }_0^2+{\left(1-v\right)}^2{\sigma }_1^2}$

where m₀, m₁, and m₂ are mean of the class 0, class 1, and class 2, and σ₀, and σ₁, and σ₂ are the standard deviation of the class 0, class 1, and class 2, respectively. The parameter w is a weighting factor for the population of the classes and the parameter v is a weighting value for the importance of the class 0 and class 1. In one embodiment of the invention, the value of the weight w is

$w=\frac{\mathrm{number}\mathrm{of}\mathrm{objects}\mathrm{of}\mathrm{class}0}{\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{objects}}$

In another embodiment of the invention, we set w=1 without considering the number of objects. In a preferred embodiment of the invention, the value of v is set to 0.5. This is the center of the distribution of the class 0 and class 1. Those ordinary skilled in the art should recognize that other values of w and v can be used and they are within the scope of this invention.

In a particularly preferred, yet not limiting embodiment, the goodness metric of the contrast boosting is defined so that it is higher if D is higher and V is lower. Three types of the rules satisfying the goodness metric properties are provided as non-limiting embodiment of the invention.

$J1=D-\gamma V$ $J2=\frac{D}{1+\gamma V}$ $J3=\hspace{0.17em}D{}^{-\gamma V}$

In one embodiment of the invention, the new feature generation rules are simply the selected initial features and pre-defined feature combination rules with its optimal boosting_factor values.

Boosting Factor Determination

The boosting factor determination method determines the boosting factor for the best linear combination of two features: Feature_1+boosting_factor*Feature_2.

Let two features be f and g, the linear combined features can be written as

h=f+αg

Where α is the boostinmg_factor.

1. Parametric Method

From the above method, the mean, variance and covariance are

m ₀ =m _0f +αm _0g

m ₁ =m _1f αm _1g

m ₂ =m ₂ +αm _2g

σ₀²=σ_0f²+2ασ_0fg+α²σ_0g²

σ₁²=σ_1f²+2ασ_1fg+α²σ_0g²

σ₁¹=σ_2f²+2ασ_2fg+α²σ_2g²

Combining the above methods, the metric D can be rewritten as follows:

$D=\frac{{\left(p_1+\alpha p_2\right)}^2}{q_1^{+2\alpha }q_2+{\alpha }^2q_3}$

and its derivative as follows:

$D^{\prime }=\frac{D}{\alpha }=\frac{2\left(p_1+\alpha p_2\right)\left[\left(p_2q_1-p_1q_2\right)+\alpha \left(p_2q_2-p_1q_3\right)\right]}{{\left(q_1+2\alpha q_2+{\alpha }^2q_3\right)}^2}$

where

p ₁ =m _0f +m _1f

p ₂ =m _0g +m _1g

q ₁ =wσ _0f ²+(1−w)σ_1f²

q ₂ =wσ _0fg+(1−w)σ_1fg

q ₃ =wσ _0g ²+(1−w)σ_1g²

and metric v can be rewritten as follows:

$V=\frac{{\left(r_1+\alpha r_2\right)}^2}{s_1+2{\alpha }_{S_2}+{\alpha }^2s_3}$

and its derivative as follows:

$V^{\prime }=\frac{V}{\alpha }=\frac{2\left(r_1+r_2\right)\left[\left(r_2s_1-r_1s_2\right)+\alpha \left(r_2s_2-r_1s_3\right)\right]}{{\left(s_12{\alpha }_{s_2}+{\alpha }^2s_3\right)}^2}$

where

r ₁ =m _2f ^−v m _0f−(1−v)m_1f

r ₂ =m _2g ^−v m _0g−(1−v)m_1g

s ₁=σ_2f²+v²σ_0f²+(1−v)²σ_1f²

s ₂=σ_2fg+v²σ_0fg+(1−v)²σ_1fg

₃=σ_2g²+v²σ_0g²+(1−v)²σ_1g²

To maximize the goodness functions, find the proper α so that

$\frac{J}{\alpha }=0.$

For each cases, the best α value is the solution of the

$\frac{J1}{\alpha }=D^{\prime }-\gamma V^{\prime }=0$ $\frac{J2}{\alpha }=\frac{D^{\prime }\left(1+\gamma V\right)-\gamma {\mathrm{DV}}^{\prime }}{{\left(1-\gamma D\right)}^2}=0$ $\frac{J3}{\alpha }=\left(D^{\prime }-\gamma {\mathrm{DV}}^{\prime }\right){}^{-\gamma V}=0$

2. Non-Parametric Method

The parametric method of finding a is under the Gaussian assumption. In many practical applications, however, the Gaussian assumption does not apply. In one embodiment of the invention, a non-parametric method using the area ROC (receiver operation curve) is applied.

In Gaussian distribution, the smaller area ROC (AR) is

AR=erfc(D)

where

$\text{erf}c\left(x\right)=\frac{1}{\sqrt{2\pi }}{\int }_x^{\infty }\exp \left(-t^2/2\right)t$

From the above relationship, we could defined:

D=erf ⁻¹(AR)

Therefore, the procedure to find the goodness metric D is

• • a Find the smallest area of ROC between the distribution of class 0 and class 1: ARD
  • b Calculate D=erf⁻¹(ARD)Finding the second goodness metric v is equivalent to finding the discrimination between distribution of class 2 and the weighted average of the distribution of the class 0 and class 1. Therefore, the procedure to get the second metric is as follows:
  • a Take data from class 0: f₀
  • b Take the data from class 1: f₁
  • c Weighted average: f₀₁=v f₀+(1−v)f₁
  • d Fond the smallest area of ROC between the distribution of class 2 and combined class 0 and 1: ARV
  • e Calculate V=erf⁻¹(ARV)

The best α is determined by maximizing the values in the above steps c, d, and e. In one embodiment of the invention, the operation of the erf⁻¹(x) is used in table or inverse function of the sigmoid functions.

3. Ranked Method

In the case that the ranking among the extreme examples is specified, one embodiment of the invention generates new features considering the ranks. The goodness metric include the integration of two metrics as follows:

JR1=E(1+γV)

JR2=E_e^γV

where E is the error estimation part of the metric and V is the class 2 part of the metric. The better feature is the one with smaller JR value.

The error estimation metric E for this case is simply related to the error of the ranks. When rank between 1 to LL and HH to N from the N objects are given, in one embodiment of the invention, the metric is

$D=\sum _{r=1}^{\mathrm{LL}}W_r\mathrm{rankofFeature}-r+\sum _{r=\mathrm{HH}}^NW_r\mathrm{rankofFeature}-r$

which uses only rank information. However, the rank misleads the contrast boosting result when feature values of the several ranks are similar. To overcome this problem, in another embodiment of the invention, the metric is

$D=\frac{\sum _{r=1}^{\mathrm{LL}}{w_r\left({\hat{f}}_r-f_r\right)}^2+\sum _{r=\mathrm{HH}}^N{w_r\left({\hat{f}}_r-f_r\right)}^2}{{\hat{f}}_{\mathrm{HQ}}-{\hat{f}}_{\mathrm{LQ}}}$

where f_r is the feature value of the given rank r and {circumflex over (f)}_r is the feature value of the sorted rank r. {circumflex over (f)}_HQ and {circumflex over (f)}_LQ are the feature values of top 25 and 75 percentile. The weight value w_r can be used for the emphasis the specific rank. For example, w_r=1 or

$w_r=\frac{N^2}{N^2+\gamma r\left(N-r\right)}$ $w_r=\frac{N}{N+\gamma \sqrt{r\left(N-r\right)}}.$

The rank of class 2 is meaningless, so the comparison of the ranking is not meaningful. Therefore, the metric of given class may be better. The procedure of this method is

• • 1. Find the mean and deviation of the rank [1, LL]: m₁, σ₁²
  • 2. Find the mean and deviation of the rank [HH, N]: m₀, σ₀²
  • 3. Find the mean and deviation of the others m₂, σ₂²
  • 4. Find the V values using the previously described formula.

The boosting factor can be determined by finding the best α to have minimum of the cost1/cost2 using the new feature f+αg .

III.3 Extreme Directed Feature Ranking

The new features and the initial features are processed to generate goodness metric using the methods described above. The goodness metrics represent extreme directed measures. Therefore, the features are ranked according to the goodness metrics. This results in the extreme ranked features for displaying to human 110.

III.4 Feature Display and Selection

The feature display and selection 912 allows human 110 to select the features based on the extreme ranked features 902. The object montage display 316 of the selected features is generated using the previously described method. The object montage display 316 is shown to human 110 along with the new feature generation rules 204 and the generating features. After object montage display 316 reviewing, the human 110 makes the selection among the initial features 106 and the new features 900 for optimal feature selection. This results in the optimized features 202. The optimized features 202 along with their new feature generation rules 204 are the feature recipe output 108 of the invention.

The invention has been described herein in considerable detail in order to comply with the Patent Statutes and to provide those skilled in the art with the information needed to apply the novel principles and to construct and use such specialized components as are required. However, it is to be understood that the inventions can be carried out by specifically different equipment and devices, and that various modifications, both as to the equipment details and operating procedures, can be accomplished without departing from the scope of the invention itself.

Claims

1. A computerized directed feature development method comprising the steps of: a) Input initial feature list, learning image and object masks;b) Perform feature measurements using the initial feature list, the learning image and the object masks having initial features output;c) Perform interactive feature enhancement by human using the initial feature list, the learning image, the object masks, and the initial features having feature recipe output.

2. The computerized directed feature development method of claim 1 wherein the interactive feature enhancement method further comprises a visual profiling selection step to generate a subset features.

3. The computerized directed feature development method of claim 1 wherein the interactive feature enhancement method further comprises a contrast boosting step to generate optimized features and new feature generation rules outputs.

4. A visual profiling selection method for computerized directed feature development comprising the steps of: a) Input initial feature list, initial features, learning image and object masks;b) Perform information measurement using the initial features having information scores output;c) Perform ranking of the initial feature list using the information scores having a ranked feature list output;d) Perform human selection through a user interface using the ranked feature list having a profiling feature output.

5. The visual profiling selection method for computerized directed feature development of claim 4 further comprises an object sorting step using the initial features and the profiling feature having an object sequence and object feature values output.

6. The visual profiling selection method for computerized directed feature development of claim 5 further comprises an object montage creation step using the learning image, the object masks, the object sequence and the object feature values having an object montage display output.

7. The visual profiling selection method for computerized directed feature development of claim 6 further performs human selection through a user interface using the object montage display having subset features output.

8. The visual profiling selection method for computerized directed feature development of claim 6 wherein the object montage creation comprising the steps of: a) Perform object zone creation using the learning image and the object masks having object zone output;b) Perform object montage synthesis using the object zone and the object sequence having object montage frame output;c) Perform object montage display creation using the object montage frame and the object feature values having object montage display output.

9. The visual profiling selection method for computerized directed feature development of claim 5 further comprises a histogram creation step using the object feature values having an histogram plot output.

10. The visual profiling selection for computerized directed feature development method of claim 9 further performs human selection through a user interface using the histogram plot having subset features output.

11. The visual profiling selection method for computerized directed feature development of claim 9 wherein the histogram creation comprising the steps of: a) Perform binning using the object feature values having bin counts and bin ranges output;b) Perform bar synthesis using the bin counts having bar charts output;c) Perform histogram plot creation using the bar charts and the bar ranges having histogram plot output.

12. A contrast boosting feature optimization method for computerized directed feature development comprising the steps of: a) Input object montage display and initial features;b) Perform extreme example specification by human using the object montage display having updated montage output;c) Perform extreme directed feature ranking using the updated montage and the initial features having extreme ranked features output.

13. The contrast boosting feature optimization method of claim 12 further performs feature display and selection by human using the extreme ranked features and initial features having optimized features output.

14. The contrast boosting feature optimization method of claim 12 wherein the extreme directed feature ranking ranks features according to their goodness metrics.

15. The contrast boosting feature optimization method of claim 14 wherein the goodness metrics consist of discrimination between class 0 and class 1 and class 2 difference.

16. The contrast boosting feature optimization method of claim 12 further performs contrast boosting feature generation using the updated montage and initial features having new features and new feature generation rules output.

17. The contrast boosting feature optimization method of claim 16 wherein the new features selected from a set consisting of weighting, normalization, and correlation.

18. The contrast boosting feature optimization method of claim 16 wherein the extreme directed feature ranking using updated montage, new features, and initial features having extreme ranked features output.

19. The contrast boosting feature optimization method of claim 18 further performs feature display and selection by human using the extreme ranked features, new features, new feature generation rules and initial features having optimized features output.

20. The contrast boosting feature generation method of claim 16 comprising the steps of: a) Perform population class construction using the updated montage and the initial features having population classes output;b) Perform new feature generation using the population classes having new features and new feature generation rules output.