Summary

We conclude that otolith age dictates which class of boundary transformation (includ- ing/excluding scale information) can be used to construct accurate classifiers. Whilst scale retaining methods show superior results for young (age 0/1) otolith samples, scale invariant transform methods perform better for older samples (age 2-5). How- ever, we find that whilst scale invariant date performs better at higher ages, results are inferior to those obtained using younger samples (Objective 2).

As with Chapter 3, we find that curvature scale space methods offer no improvement on Fourier techniques (Objective 1), even given additional choice learning algorithms used to construct classifiers (Objective 4). However, we note that there was no

noticeable drop in classification accuracies when the datasets used (as single age sets) were considerably smaller than in previous study (Chapter 3).

Susceptibility Of Fourier Based

Classification To Outlining

Methods

This chapter examines whether the method chosen for otolith boundary extraction affects stock discrimination accuracies using elliptical Fourier based classification. The work presented here focuses mainly on our third objective (Chapter 1, Section 1.2) and also pertains to Objective 4, whether learning algorithm affects extraction method choice. Boundaries extracted by two experts using two outlining methods were used to construct classifiers then used to assign further samples to their source populations.

Previous studies have used variable methods of boundary determination prior to Fourier based morphological classification. In the studies reported by Begg and Brown [11] and Begg et al. [12] otoliths were outlined by hand and transformed using Fourier methods before discriminant analysis was used to predict further samples, with classi- fication accuracies in the range 56-81% dependent on whether single-age or multi-age tasks were undertaken. Results from DeVries et al. [30] also fell firmly within this ac- curacy range for Atlantic mackerel stock classification at 77.5% using elliptical Fourier descriptors and discriminant analysis.

Whilst the study by Burke et al. [22] reported accuracies far above this range at 97%, we were unable to replicate this result using similar methods described in the Chapter 4, with results consistently falling into the range above (56-81%) when using size normalised Fourier descriptors. However, Burke et al. [22] details hand-tracing of annuli in their study, whilst in the study presented in Chapter 4 samples are outlined automatically using thresholding methods.

Methods of automatically extracting the boundary from the digitized otolith have also been used with varying success. Atlantic cod was successfully separated using Fourier methods built on outlines automatically outlined using a threshold deter- mined with analysis of the grey-scale histogram [25], reporting accuracies of 85-96% dependent on age class. In Stransky [90] classification of redfish returned mixed results, however accuracies for certain tasks were reported as high as 76% overall accuracy. However these results were for regional separation, and in the same study mixed accuracies were reported for stock separation, depending on the species being analysed.

We therefore compare two methods of boundary extraction in this study: Outlines derived by two expert readers, traced by hand; and outlines derived by intensity thresholding the otolith image using two different approaches. As in the chapter 4 our goal is not to achieve high classification rates, rather we assess how outlines created using each method differ when used to construct and test classifiers. To this end outlines from each method are transformed using elliptical Fourier methods creating a set of harmonics for each of the outlining methods. As in previous work we construct classifiers, each fully cross validated, using varying harmonic content and multiple learning algorithms available within the WEKA machine learning suite, allowing us to determine whether algorithm selection is dependent on outlining methods used.

We find that classifiers constructed using otolith boundaries traced by hand marginally outperform those that use boundaries extracted using thresholding methods, and that classifiers may be constructed by one expert and used to test further outlined sam- ples (outlined by the alternative expert) with a statistically insignificant decrease in accuracy. The choice of learning algorithm used to construct classifiers is also an im- portant consideration, with three algorithms performing well regardless of the outline methods used. However we also note that algorithm selection may significantly differ given additional classification tasks.

5.1

Materials and Methods

Whilst most outlining, transform and classification methods used in this chapter are detailed in Chapter 2, details of how the methods were used with regards to the study presented in this chapter are given here.

Two expert readers were employed to perform otolith outlining on a small image set (described in Section 5.1.1) using two different methods: hand outlining; and user defined thresholding. These methods are used to compile four sets of image outlines: Two for expert outlines (one set per expert) and two thresholded out- lines (High and Low thresholds). The specific methods for these are detailed in Sections 5.1.2 and 5.1.3. With the respective sections also providing information on how each of the outline sets are used to build and test classifiers.

The choice of harmonic content, and the learning algorithms used for building and testing classifiers are discussed in Sections 5.1.4 and 5.1.5, followed by details of statistical tests used to analyse the results of tests in Section 5.1.6

It should be noted that when we refer to an ‘expert’ we refer to both the expert user, and that user’s computing set-up. For example, both experts used the same software to perform the tasks, however, expert-1 used high precision hardware for the tasks (high precision mouse/mouse-mat etc.), whilst expert-2 used altogether more standard equipment to perform outlining tasks.