Classification of boar sperm head images using Learning Vector Quantization Rijksuniversiteit Groningen/ NL Mathematics and Computing Science Michael Biehl, Piter Pasma, Marten Pijl, Nicolai Petkov Lidia Sánchez University of León / Spain Electrical and Electronical Engineering

ESANN 2006, Classification of boar sperm head images using LVQ semen fertility assessment: important problem in human / veterinary medicine medical diagnosis: - sophisticated techniques, e.g. staining methods - high accurracy determination of fertility evaluation of sample quality for animal breeding purposes - fast and cheap method of inspection Motivation here: - microscopic images of boar sperm heads (Leon/Spain) e.g. quality inspection after freezing and storage - distance-based classification, parameterized by prototypes - Learning Vector Quantization + Relevance Learning

ESANN 2006, Classification of boar sperm head images using LVQ microscopic images of boar sperms preprocessing: - isolate and align head images - normalize with respect to mean grey level and corresponding variance - resize and approximate by an ellipsoidal region of 19x35 pixels - replace “missing” pixels (black) by the overall mean grey level

ESANN 2006, Classification of boar sperm head images using LVQ normal (650) non-normal (710) 1360 example images, classified by experts (visual inspection) application of Learning Vector Quantization: - prototypes determined from example data - parameterize a distance based classification - plausible, straightforward to interpret/discuss with experts - include adaptive metrics in relevance learning

ESANN 2006, Classification of boar sperm head images using LVQ identify the closest prototype, i.e the so-called winner initialize prototype vectors for different classes present a single example move the winner - closer towards the data (same class) - away from the data (different class) classification: assignment of a vector  to the class of the closest prototype w    aim: generalization ability classification of novel data after learning from examples Learning Vector Quantization (LVQ) example: basic scheme LVQ1 [Kohonen] 

ESANN 2006, Classification of boar sperm head images using LVQ Euclidean distance between data ξ prototype w: LVQ1 given ξ, update only the winner: decreasing learning rate : Learning algorithms (sign acc. to class membership) prototype initialization: class-conditional means + random displacement (∼70% correct classification)

ESANN 2006, Classification of boar sperm head images using LVQ example outcome: LVQ1 with 4 prototypes for each class: normalnon-normal cross-validation scheme evaluation of performance - with respect to the training data, e.g. 90% of all data - with respect to test data 10% of all data average outcome over 10 realizations

ESANN 2006, Classification of boar sperm head images using LVQ comparison of different LVQ systems (# of prototypes) ten-fold cross-validation: non-normal normal performance on training data … improves with increasing number of (non-normal) prototypes % correct non-normal normal performance w.r.t. test data … depends only weakly on the considered number of prototypes % correct

ESANN 2006, Classification of boar sperm head images using LVQ Generalized Learning Vector Quantization (GLVQ) given a single example, update the two winning prototypes : w J from the same class as the example (correct winner) w K from the other class (wrong winner) perform gradient descent steps with respect to an instantaneous cost function f(z) [A.S. Sato and K. Yamada, NIPS 7, 1995)]

ESANN 2006, Classification of boar sperm head images using LVQ Generalized Relevance LVQ (GRLVQ) GLVQ with modified distance measure vector of relevances, normalization GRLVQ - determines favorable positions of the prototypes - adapts the corresponding distance measure [B. Hammer, T. Villmann, Neural Networks 15: ] - re-define cost function f(z) in terms of d λ : - perform gradient steps w.r.t. prototypes w J, w K and vector λ

ESANN 2006, Classification of boar sperm head images using LVQ Comparison of performance: estimated test error LVQ % (4.0) 81.6 % (4.5) GLVQ 75.6 % (4.1) 76.4 % (3.8) GRLVQ81.5 % (3.5) 81.7 % (3.7) alg. 3/3 1/7 normal/non-normal prototypes - weak dependence on the number of prototypes - inferior performance of GLVQ (cost function ↮ classification error) - recovered when including relevances mean (stand. dev.)

ESANN 2006, Classification of boar sperm head images using LVQ - only very few pixels are sufficient for successful classification test error: (all) 82.75%, (69) 82.75%, (15) 81.87% GRLVQ: resulting relevances normal non-normal (LVQ1 prototypes)

ESANN 2006, Classification of boar sperm head images using LVQ Summary LVQ provides a transparent, plausible classification of microscopic boar sperm head images Performance: LVQ1 ↘ GLVQ ↗ GRLVQ satisfactory classification error (ultimate goal: estimation of sample composition) Relevances: very few relevant pixels, robust performance noisy labels / insufficient resolution? Outlook - improve LVQ system, algorithms, relevance schemes - training data, objective classification (staining method) - classification based on contour information (gradient profile)

ESANN 2006, Classification of boar sperm head images using LVQ LVQ1 demo